face recognition phd thesis

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Towards three-dimensional face recognition in the real

Vers une reconnaissance faciale tridimensionnelle dans le réel.

• UL2 - Université Lumière - Lyon 2 (86, rue Pasteur - 69007 Lyon - France) 33804
• Université de Lyon (92 rue Pasteur - CS 30122, 69361 Lyon Cedex 07 - France) 301088
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Face recognition systems: a survey.

1. Introduction

• We first introduced face recognition as a biometric technique.
• We presented the state of the art of the existing face recognition techniques classified into three approaches: local, holistic, and hybrid.
• The surveyed approaches were summarized and compared under different conditions.
• We presented the most popular face databases used to test these approaches.
• We highlighted some new promising research directions.

2. Face Recognition Systems Survey

2.1. essential steps of face recognition systems.

• Face Detection : The face recognition system begins first with the localization of the human faces in a particular image. The purpose of this step is to determine if the input image contains human faces or not. The variations of illumination and facial expression can prevent proper face detection. In order to facilitate the design of a further face recognition system and make it more robust, pre-processing steps are performed. Many techniques are used to detect and locate the human face image, for example, Viola–Jones detector [ 24 , 25 ], histogram of oriented gradient (HOG) [ 13 , 26 ], and principal component analysis (PCA) [ 27 , 28 ]. Also, the face detection step can be used for video and image classification, object detection [ 29 ], region-of-interest detection [ 30 ], and so on.
• Feature Extraction : The main function of this step is to extract the features of the face images detected in the detection step. This step represents a face with a set of features vector called a “signature” that describes the prominent features of the face image such as mouth, nose, and eyes with their geometry distribution [ 31 , 32 ]. Each face is characterized by its structure, size, and shape, which allow it to be identified. Several techniques involve extracting the shape of the mouth, eyes, or nose to identify the face using the size and distance [ 3 ]. HOG [ 33 ], Eigenface [ 34 ], independent component analysis (ICA), linear discriminant analysis (LDA) [ 27 , 35 ], scale-invariant feature transform (SIFT) [ 23 ], gabor filter, local phase quantization (LPQ) [ 36 ], Haar wavelets, Fourier transforms [ 31 ], and local binary pattern (LBP) [ 3 , 10 ] techniques are widely used to extract the face features.
• Face Recognition : This step considers the features extracted from the background during the feature extraction step and compares it with known faces stored in a specific database. There are two general applications of face recognition, one is called identification and another one is called verification. During the identification step, a test face is compared with a set of faces aiming to find the most likely match. During the identification step, a test face is compared with a known face in the database in order to make the acceptance or rejection decision [ 7 , 19 ]. Correlation filters (CFs) [ 18 , 37 , 38 ], convolutional neural network (CNN) [ 39 ], and also k-nearest neighbor (K-NN) [ 40 ] are known to effectively address this task.

2.2. Classification of Face Recognition Systems

3. local approaches, 3.1. local appearance-based techniques.

• Local binary pattern (LBP) and it’s variant: LBP is a great general texture technique used to extract features from any object [ 16 ]. It has widely performed in many applications such as face recognition [ 3 ], facial expression recognition, texture segmentation, and texture classification. The LBP technique first divides the facial image into spatial arrays. Next, within each array square, a 3 × 3 pixel matrix ( p 1 … … p 8 ) is mapped across the square. The pixel of this matrix is a threshold with the value of the center pixel ( p 0 ) (i.e., use the intensity value of the center pixel i ( p 0 ) as a reference for thresholding) to produce the binary code. If a neighbor pixel’s value is lower than the center pixel value, it is given a zero; otherwise, it is given one. The binary code contains information about the local texture. Finally, for each array square, a histogram of these codes is built, and the histograms are concatenated to form the feature vector. The LBP is defined in a matrix of size 3 × 3, as shown in Equation (1). LBP = ∑ p = 1 8 2 p s ( i 0 − i p ) ,      w i t h   s ( x ) = { 1 x ≥ 0 0 x < 0 , (1) where i 0 and i p are the intensity value of the center pixel and neighborhood pixels, respectively. Figure 3 illustrates the procedure of the LBP technique. Khoi et al. [ 20 ] propose a fast face recognition system based on LBP, pyramid of local binary pattern (PLBP), and rotation invariant local binary pattern (RI-LBP). Xi et al. [ 15 ] have introduced a new unsupervised deep learning-based technique, called local binary pattern network (LBPNet), to extract hierarchical representations of data. The LBPNet maintains the same topology as the convolutional neural network (CNN). The experimental results obtained using the public benchmarks (i.e., LFW and FERET) have shown that LBPNet is comparable to other unsupervised techniques. Laure et al. [ 40 ] have implemented a method that helps to solve face recognition issues with large variations of parameters such as expression, illumination, and different poses. This method is based on two techniques: LBP and K-NN techniques. Owing to its invariance to the rotation of the target image, LBP become one of the important techniques used for face recognition. Bonnen et al. [ 42 ] proposed a variant of the LBP technique named “multiscale local binary pattern (MLBP)” for features’ extraction. Another LBP extension is the local ternary pattern (LTP) technique [ 43 ], which is less sensitive to the noise than the original LBP technique. This technique uses three steps to compute the differences between the neighboring ones and the central pixel. Hussain et al. [ 36 ] develop a local quantized pattern (LQP) technique for face representation. LQP is a generalization of local pattern features and is intrinsically robust to illumination conditions. The LQP features use the disk layout to sample pixels from the local neighborhood and obtain a pair of binary codes using ternary split coding. These codes are quantized, with each one using a separately learned codebook.
• Histogram of oriented gradients (HOG) [ 44 ]: The HOG is one of the best descriptors used for shape and edge description. The HOG technique can describe the face shape using the distribution of edge direction or light intensity gradient. The process of this technique done by sharing the whole face image into cells (small region or area); a histogram of pixel edge direction or direction gradients is generated of each cell; and, finally, the histograms of the whole cells are combined to extract the feature of the face image. The feature vector computation by the HOG descriptor proceeds as follows [ 10 , 13 , 26 , 45 ]: firstly, divide the local image into regions called cells, and then calculate the amplitude of the first-order gradients of each cell in both the horizontal and vertical direction. The most common method is to apply a 1D mask, [–1 0 1]. G x ( x ,   y ) = I ( x + 1 ,   y ) − I ( x − 1 ,   y ) , (2) G y ( x ,   y ) = I ( x ,   y + 1 ) − I ( x ,   y − 1 ) , (3) where I ( x ,   y ) is the pixel value of the point ( x ,   y ) and G x ( x ,   y ) and G y ( x ,   y ) denote the horizontal gradient amplitude and the vertical gradient amplitude, respectively. The magnitude of the gradient and the orientation of each pixel ( x , y ) are computed as follows: G ( x ,   y ) = G x ( x ,   y ) 2 + G y ( x ,   y ) 2 , (4) θ ( x ,   y ) = tan − 1 ( G y ( x ,   y ) G x ( x ,   y ) ) . (5) The magnitude of the gradient and the orientation of each pixel in the cell are voted in nine bins with the tri-linear interpolation. The histograms of each cell are generated pixel based on direction gradients and, finally, the histograms of the whole cells are combined to extract the feature of the face image. Karaaba et al. [ 44 ] proposed a combination of different histograms of oriented gradients (HOG) to perform a robust face recognition system. This technique is named “multi-HOG”. The authors create a vector of distances between the target and the reference face images for identification. Arigbabu et al. [ 46 ] proposed a novel face recognition system based on the Laplacian filter and the pyramid histogram of gradient (PHOG) descriptor. In addition, to investigate the face recognition problem, support vector machine (SVM) is used with different kernel functions.
• Correlation filters: Face recognition systems based on the correlation filter (CF) have given good results in terms of robustness, location accuracy, efficiency, and discrimination. In the field of facial recognition, the correlation techniques have attracted great interest since the first use of an optical correlator [ 47 ]. These techniques provide the following advantages: high ability for discrimination, desired noise robustness, shift-invariance, and inherent parallelism. On the basis of these advantages, many optoelectronic hybrid solutions of correlation filters (CFs) have been introduced such as the joint transform correlator (JTC) [ 48 ] and VanderLugt correlator (VLC) [ 47 ] techniques. The purpose of these techniques is to calculate the degree of similarity between target and reference images. The decision is taken by the detection of a correlation peak. Both techniques (VLC and JTC) are based on the “ 4 f ” optical configuration [ 37 ]. This configuration is created by two convergent lenses ( Figure 4 ). The face image F is processed by the fast Fourier transform (FFT) based on the first lens in the Fourier plane S F . In this Fourier plane, a specific filter P is applied (for example, the phase-only filter (POF) filter [ 2 ]) using optoelectronic interfaces. Finally, to obtain the filtered face image F ′ (or the correlation plane), the inverse FFT (IFFT) is made with the second lens in the output plane. For example, the VLC technique is done by two cascade Fourier transform structures realized by two lenses [ 4 ], as presented in Figure 5 . The VLC technique is presented as follows: firstly, a 2D-FFT is applied to the target image to get a target spectrum S . After that, a multiplication between the target spectrum and the filter obtain with the 2D-FFT of a reference image is affected, and this result is placed in the Fourier plane. Next, it provides the correlation result recorded on the correlation plane, where this multiplication is affected by inverse FF. The correlation result, described by the peak intensity, is used to determine the similarity degree between the target and reference images. C = F F T − 1 { S ∗ ∘ P O F } , (6) where F F T − 1 stands for the inverse fast FT (FFT) operation, * represents the conjugate operation, and ∘ denotes the element-wise array multiplication. To enhance the matching process, Horner and Gianino [ 49 ] proposed a phase-only filter (POF). The POF filter can produce correlation peaks marked with enhanced discrimination capability. The POF is an optimized filter defined as follows: H P O F ( u , v ) = S ∗ ( u , v ) | S ( u , v ) | , (7) where S ∗ ( u , v ) is the complex conjugate of the 2D-FFT of the reference image. To evaluate the decision, the peak to correlation energy (PCE) is defined as the energy in the correlation peaks’ intensity normalized to the overall energy of the correlation plane. P C E = ∑ i , j N E p e a k ( i , j ) ∑ i , j M E c o r r e l a t i o n − p l a n e ( i , j ) , (8) where i , j are the coefficient coordinates; M and N are the size of the correlation plane and the size of the peak correlation spot, respectively; E p e a k is the energy in the correlation peaks; and E c o r r e l a t i o n − p l a n e is the overall energy of the correlation plane. Correlation techniques are widely applied in recognition and identification applications [ 4 , 37 , 50 , 51 , 52 , 53 ]. For example, in the work of [ 4 ], the authors presented the efficiency performances of the VLC technique based on the “4f” configuration for identification using GPU Nvidia Geforce 8400 GS. The POF filter is used for the decision. Another important work in this area of research is presented by Leonard et al. [ 50 ], which presented good performance and the simplicity of the correlation filters for the field of face recognition. In addition, many specific filters such as POF, BPOF, Ad, IF, and so on are used to select the best filter based on its sensitivity to the rotation, scale, and noise. Napoléon et al. [ 3 ] introduced a novel system for identification and verification fields based on an optimized 3D modeling under different illumination conditions, which allows reconstructing faces in different poses. In particular, to deform the synthetic model, an active shape model for detecting a set of key points on the face is proposed in Figure 6 . The VanderLugt correlator is proposed to perform the identification and the LBP descriptor is used to optimize the performances of a correlation technique under different illumination conditions. The experiments are performed on the Pointing Head Pose Image Database (PHPID) database with an elevation ranging from −30° to +30°.

3.2. Key-Points-Based Techniques

• Scale invariant feature transform (SIFT) [ 56 , 57 ]: SIFT is an algorithm used to detect and describe the local features of an image. This algorithm is widely used to link two images by their local descriptors, which contain information to make a match between them. The main idea of the SIFT descriptor is to convert the image into a representation composed of points of interest. These points contain the characteristic information of the face image. SIFT presents invariance to scale and rotation. It is commonly used today and fast, which is essential in real-time applications, but one of its disadvantages is the time of matching of the critical points. The algorithm is realized in four steps: (1) detection of the maximum and minimum points in the space-scale, (2) location of characteristic points, (3) assignment of orientation, and (4) a descriptor of the characteristic point. A framework to detect the key-points based on the SIFT descriptor was proposed by L. Lenc et al. [ 56 ], where they use the SIFT technique in combination with a Kepenekci approach for the face recognition.
• Speeded-up robust features (SURF) [ 29 , 57 ]: the SURF technique is inspired by SIFT, but uses wavelets and an approximation of the Hessian determinant to achieve better performance [ 29 ]. SURF is a detector and descriptor that claims to achieve the same, or even better, results in terms of repeatability, distinction, and robustness compared with the SIFT descriptor. The main advantage of SURF is the execution time, which is less than that used by the SIFT descriptor. Besides, the SIFT descriptor is more adapted to describe faces affected by illumination conditions, scaling, translation, and rotation [ 57 ]. To detect feature points, SURF seeks to find the maximum of an approximation of the Hessian matrix using integral images to dramatically reduce the processing computational time. Figure 7 shows an example of SURF descriptor for face recognition using AR face datasets [ 58 ].
• Binary robust independent elementary features (BRIEF) [ 30 , 57 ]: BRIEF is a binary descriptor that is simple and fast to compute. This descriptor is based on the differences between the pixel intensity that are similar to the family of binary descriptors such as binary robust invariant scalable (BRISK) and fast retina keypoint (FREAK) in terms of evaluation. To reduce noise, the BRIEF descriptor smoothens the image patches. After that, the differences between the pixel intensity are used to represent the descriptor. This descriptor has achieved the best performance and accuracy in pattern recognition.
• Fast retina keypoint (FREAK) [ 57 , 59 ]: the FREAK descriptor proposed by Alahi et al. [ 59 ] uses a retinal sampling circular grid. This descriptor uses 43 sampling patterns based on retinal receptive fields that are shown in Figure 8 . To extract a binary descriptor, these 43 receptive fields are sampled by decreasing factors as the distance from the thousand potential pairs to a patch’s center yields. Each pair is smoothed with Gaussian functions. Finally, the binary descriptors are represented by setting a threshold and considering the sign of differences between pairs.

3.3. Summary of Local Approaches

4. holistic approach, 4.1. linear techniques.

• Eigenface [ 34 ] and principal component analysis (PCA) [ 27 , 62 ]: Eigenfaces is one of the popular methods of holistic approaches used to extract features points of the face image. This approach is based on the principal component analysis (PCA) technique. The principal components created by the PCA technique are used as Eigenfaces or face templates. The PCA technique transforms a number of possibly correlated variables into a small number of incorrect variables called “principal components”. The purpose of PCA is to reduce the large dimensionality of the data space (observed variables) to the smaller intrinsic dimensionality of feature space (independent variables), which are needed to describe the data economically. Figure 9 shows how the face can be represented by a small number of features. PCA calculates the Eigenvectors of the covariance matrix, and projects the original data onto a lower dimensional feature space, which are defined by Eigenvectors with large Eigenvalues. PCA has been used in face representation and recognition, where the Eigenvectors calculated are referred to as Eigenfaces (as shown in Figure 10 ). An image may also be considering the vector of dimension M × N , so that a typical image of size 4 × 4 becomes a vector of dimension 16. Let the training set of images be { X 1 , X 2 ,   X 3 …   X N } . The average face of the set is defined by the following: X ¯ = 1 N ∑ i = 1 N X   i . (9) Calculate the estimate covariance matrix to represent the scatter degree of all feature vectors related to the average vector. The covariance matrix Q is defined by the following: Q = 1 N ∑ i = 1 N ( X ¯ − X i ) ( X ¯ − X i ) T . (10) The Eigenvectors and corresponding Eigen-values are computed using C V = λ V ,       ( V ϵ R n ,   V ≠ 0 ) , (11) where V is the set of eigenvectors matrix Q associated with its eigenvalue λ . Project all the training images of i t h person to the corresponding Eigen-subspace: y k i = w T    ( x i ) ,       ( i = 1 ,   2 ,   3   …   N ) , (12) where the y k i are the projections of x and are called the principal components, also known as eigenfaces. The face images are represented as a linear combination of these vectors’ “principal components”. In order to extract facial features, PCA and LDA are two different feature extraction algorithms that are used. Wavelet fusion and neural networks are applied to classify facial features. The ORL database is used for evaluation. Figure 10 shows the first five Eigenfaces constructed from the ORL database [ 63 ].
• Fisherface and linear discriminative analysis (LDA) [ 64 , 65 ]: The Fisherface method is based on the same principle of similarity as the Eigenfaces method. The objective of this method is to reduce the high dimensional image space based on the linear discriminant analysis (LDA) technique instead of the PCA technique. The LDA technique is commonly used for dimensionality reduction and face recognition [ 66 ]. PCA is an unsupervised technique, while LDA is a supervised learning technique and uses the data information. For all samples of all classes, the within-class scatter matrix S W and the between-class scatter matrix S B are defined as follows: S B = ∑ I = 1 C M i ( x i − μ ) ( x i − μ ) T , (13) S w = ∑ I = 1 C ∑ x k ϵ X i M i ( x k − μ ) ( x k − μ ) T , (14) where μ is the mean vector of samples belonging to class i , X i represents the set of samples belonging to class i with x k being the number image of that class, c is the number of distinct classes, and M i is the number of training samples in class i . S B describes the scatter of features around the overall mean for all face classes and S w describes the scatter of features around the mean of each face class. The goal is to maximize the ratio d e t | S B | / d e t | S w |, in other words, minimizing S w while maximiz ing   S B . Figure 11 shows the first five Eigenfaces and Fisherfaces obtained from the ORL database [ 63 ].
• Independent component analysis (ICA) [ 35 ]: The ICA technique is used for the calculation of the basic vectors of a given space. The goal of this technique is to perform a linear transformation in order to reduce the statistical dependence between the different basic vectors, which allows the analysis of independent components. It is determined that they are not orthogonal to each other. In addition, the acquisition of images from different sources is sought in uncorrelated variables, which makes it possible to obtain greater efficiency, because ICA acquires images within statistically independent variables.
• Improvements of the PCA, LDA, and ICA techniques: To improve the linear subspace techniques, many types of research are developed. Z. Cui et al. [ 67 ] proposed a new spatial face region descriptor (SFRD) method to extract the face region, and to deal with noise variation. This method is described as follows: divide each face image in many spatial regions, and extract token-frequency (TF) features from each region by sum-pooling the reconstruction coefficients over the patches within each region. Finally, extract the SFRD for face images by applying a variant of the PCA technique called “whitened principal component analysis (WPCA)” to reduce the feature dimension and remove the noise in the leading eigenvectors. Besides, the authors in [ 68 ] proposed a variant of the LDA called probabilistic linear discriminant analysis (PLDA) to seek directions in space that have maximum discriminability, and are hence most suitable for both face recognition and frontal face recognition under varying pose.
• Gabor filters: Gabor filters are spatial sinusoids located by a Gaussian window that allows for extracting the features from images by selecting their frequency, orientation, and scale. To enhance the performance under unconstrained environments for face recognition, Gabor filters are transformed according to the shape and pose to extract the feature vectors of face image combined with the PCA in the work of [ 69 ]. The PCA is applied to the Gabor features to remove the redundancies and to get the best face images description. Finally, the cosine metric is used to evaluate the similarity.
• Frequency domain analysis [ 70 , 71 ]: Finally, the analysis techniques in the frequency domain offer a representation of the human face as a function of low-frequency components that present high energy. The discrete Fourier transform (DFT), discrete cosine transform (DCT), or discrete wavelet transform (DWT) techniques are independent of the data, and thus do not require training.
• Discrete wavelet transform (DWT): Another linear technique used for face recognition. In the work of [ 70 ], the authors used a two-dimensional discrete wavelet transform (2D-DWT) method for face recognition using a new patch strategy. A non-uniform patch strategy for the top-level’s low-frequency sub-band is proposed by using an integral projection technique for two top-level high-frequency sub-bands of 2D-DWT based on the average image of all training samples. This patch strategy is better for retaining the integrity of local information, and is more suitable to reflect the structure feature of the face image. When constructing the patching strategy using the testing and training samples, the decision is performed using the neighbor classifier. Many databases are used to evaluate this method, including Labeled Faces in Wild (LFW), Extended Yale B, Face Recognition Technology (FERET), and AR.
• Discrete cosine transform (DCT) [ 71 ] can be used for global and local face recognition systems. DCT is a transformation that represents a finite sequence of data as the sum of a series of cosine functions oscillating at different frequencies. This technique is widely used in face recognition systems [ 71 ], from audio and image compression to spectral methods for the numerical resolution of differential equations. The required steps to implement the DCT technique are presented as follows.

4.2. Nonlinear Techniques

• Kernel linear discriminant analysis (KDA) [ 73 ]: the KLDA technique is a kernel extension of the linear LDA technique, in the same kernel extension of PCA. Arashloo et al. [ 73 ] proposed a nonlinear binary class-specific kernel discriminant analysis classifier (CS-KDA) based on the spectral regression kernel discriminant analysis. Other nonlinear techniques have also been used in the context of facial recognition:
• Gabor-KLDA [ 74 ].
• Evolutionary weighted principal component analysis (EWPCA) [ 75 ].
• Kernelized maximum average margin criterion (KMAMC), SVM, and kernel Fisher discriminant analysis (KFD) [ 76 ].
• Wavelet transform (WT), radon transform (RT), and cellular neural networks (CNN) [ 77 ].
• Joint transform correlator-based two-layer neural network [ 78 ].
• Kernel Fisher discriminant analysis (KFD) and KPCA [ 79 ].
• Locally linear embedding (LLE) and LDA [ 80 ].
• Nonlinear locality preserving with deep networks [ 81 ].
• Nonlinear DCT and kernel discriminative common vector (KDCV) [ 82 ].

4.3. Summary of Holistic Approaches

5. hybrid approach, 5.1. technique presentation.

• Gabor wavelet and linear discriminant analysis (GW-LDA) [ 91 ]: Fathima et al. [ 91 ] proposed a hybrid approach combining Gabor wavelet and linear discriminant analysis (HGWLDA) for face recognition. The grayscale face image is approximated and reduced in dimension. The authors have convolved the grayscale face image with a bank of Gabor filters with varying orientations and scales. After that, a subspace technique 2D-LDA is used to maximize the inter-class space and reduce the intra-class space. To classify and recognize the test face image, the k-nearest neighbour (k-NN) classifier is used. The recognition task is done by comparing the test face image feature with each of the training set features. The experimental results show the robustness of this approach in different lighting conditions.
• Over-complete LBP (OCLBP), LDA, and within class covariance normalization (WCCN): Barkan et al. [ 92 ] proposed a new representation of face image based over-complete LBP (OCLBP). This representation is a multi-scale modified version of the LBP technique. The LDA technique is performed to reduce the high dimensionality representations. Finally, the within class covariance normalization (WCCN) is the metric learning technique used for face recognition.
• Advanced correlation filters and Walsh LBP (WLBP): Juefei et al. [ 93 ] implemented a single-sample periocular-based alignment-robust face recognition technique based on high-dimensional Walsh LBP (WLBP). This technique utilizes only one sample per subject class and generates new face images under a wide range of 3D rotations using the 3D generic elastic model, which is both accurate and computationally inexpensive. The LFW database is used for evaluation, and the proposed method outperformed the state-of-the-art algorithms under four evaluation protocols with a high accuracy of 89.69%.
• Multi-sub-region-based correlation filter bank (MS-CFB): Yan et al. [ 94 ] propose an effective feature extraction technique for robust face recognition, named multi-sub-region-based correlation filter bank (MS-CFB). MS-CFB extracts the local features independently for each face sub-region. After that, the different face sub-regions are concatenated to give optimal overall correlation outputs. This technique reduces the complexity, achieves higher recognition rates, and provides a better feature representation for recognition compared with several state-of-the-art techniques on various public face databases.
• SIFT features, Fisher vectors, and PCA: Simonyan et al. [ 64 ] have developed a novel method for face recognition based on the SIFT descriptor and Fisher vectors. The authors propose a discriminative dimensionality reduction owing to the high dimensionality of the Fisher vectors. After that, these vectors are projected into a low dimensional subspace with a linear projection. The objective of this methodology is to describe the image based on dense SIFT features and Fisher vectors encoding to achieve high performance on the challenging LFW dataset in both restricted and unrestricted settings.
• CNNs and stacked auto-encoder (SAE) techniques: Ding et al. [ 95 ] proposed multimodal deep face representation (MM-DFR) framework based on convolutional neural networks (CNNs) technique from the original holistic face image, rendered frontal face by 3D face model (stand for holistic facial features and local facial features, respectively), and uniformly sampled image patches. The proposed MM-DFR framework has two steps: a CNNs technique is used to extract the features and a three-layer stacked auto-encoder (SAE) technique is employed to compress the high-dimensional deep feature into a compact face signature. The LFW database is used to evaluate the identification performance of MM-DFR. The flowchart of the proposed MM-DFR framework is shown in Figure 12 .
• PCA and ANFIS: Sharma et al. [ 96 ] propose an efficient pose-invariant face recognition system based on PCA technique and ANFIS classifier. The PCA technique is employed to extract the features of an image, and the ANFIS classifier is developed for identification under a variety of pose conditions. The performance of the proposed system based on PCA–ANFIS is better than ICA–ANFIS and LDA–ANFIS for the face recognition task. The ORL database is used for evaluation.
• DCT and PCA: Ojala et al. [ 97 ] develop a fast face recognition system based on DCT and PCA techniques. Genetic algorithm (GA) technique is used to extract facial features, which allows to remove irrelevant features and reduces the number of features. In addition, the DCT–PCA technique is used to extract the features and reduce the dimensionality. The minimum Euclidian distance (ED) as a measurement is used for the decision. Various face databases are used to demonstrate the effectiveness of this system.
• PCA, SIFT, and iterative closest point (ICP): Mian et al. [ 98 ] present a multimodal (2D and 3D) face recognition system based on hybrid matching to achieve efficiency and robustness to facial expressions. The Hotelling transform is performed to automatically correct the pose of a 3D face using its texture. After that, in order to form a rejection classifier, a novel 3D spherical face representation (SFR) in conjunction with the SIFT descriptor is used, which provide efficient recognition in the case of large galleries by eliminating a large number of candidates’ faces. A modified iterative closest point (ICP) algorithm is used for the decision. This system is less sensitive and robust to facial expressions, which achieved a 98.6% verification rate and 96.1% identification rate on the complete FRGC v2 database.
• PCA, local Gabor binary pattern histogram sequence (LGBPHS), and GABOR wavelets: Cho et al. [ 99 ] proposed a computationally efficient hybrid face recognition system that employs both holistic and local features. The PCA technique is used to reduce the dimensionality. After that, the local Gabor binary pattern histogram sequence (LGBPHS) technique is employed to realize the recognition stage, which proposed to reduce the complexity caused by the Gabor filters. The experimental results show a better recognition rate compared with the PCA and Gabor wavelet techniques under illumination variations. The Extended Yale Face Database B is used to demonstrate the effectiveness of this system.
• PCA and Fisher linear discriminant (FLD) [ 100 , 101 ]: Sing et al. [ 101 ] propose a novel hybrid technique for face representation and recognition, which exploits both local and subspace features. In order to extract the local features, the whole image is divided into a sub-regions, while the global features are extracted directly from the whole image. After that, PCA and Fisher linear discriminant (FLD) techniques are introduced on the fused feature vector to reduce the dimensionality. The CMU-PIE, FERET, and AR face databases are used for the evaluation.
• SPCA–KNN [ 102 ]: Kamencay et al. [ 102 ] develop a new face recognition method based on SIFT features, as well as PCA and KNN techniques. The Hessian–Laplace detector along with SPCA descriptor is performed to extract the local features. SPCA is introduced to identify the human face. KNN classifier is introduced to identify the closest human faces from the trained features. The results of the experiment have a recognition rate of 92% for the unsegmented ESSEX database and 96% for the segmented database (700 training images).
• Convolution operations, LSTM recurrent units, and ELM classifier [ 103 ]: Sun et al. [ 103 ] propose a hybrid deep structure called CNN–LSTM–ELM in order to achieve sequential human activity recognition (HAR). Their proposed CNN–LSTM–ELM structure is evaluated using the OPPORTUNITY dataset, which contains 46,495 training samples and 9894 testing samples, and each sample is a sequence. The model training and testing runs on a GPU with 1536 cores, 1050 MHz clock speed, and 8 GB RAM. The flowchart of the proposed CNN–LSTM–ELM structure is shown in Figure 13 [ 103 ].

5.2. Summary of Hybrid Approaches

6. assessment of face recognition approaches, 6.1. measures of similarity or distances.

• Peak-to-correlation energy (PCE) or peak-to-sidelobe ratio (PSR) [ 18 ]: The PCE was introduced in (8).
• Euclidean distance [ 54 ]: The Euclidean distance is one of the most basic measures used to compute the direct distance between two points in a plane. If we have two points P 1 and P 2 , with the coordinates ( x 1 ,   y 1 ) and ( x 2 ,   y 2 ) , respectively, the calculation of the Euclidean distance between them would be as follows: d E ( P 1 ,   P 2   ) = ( x 2 − x 1 ) 2 + ( y 2 − y 1 ) 2 . (15) In general, the Euclidean distance between two points P = ( 1 ,   p 2 ,   … ,   p n ) and Q = ( q 1 ,   q 2 , …   ,   q n ) in the n-dimensional space would be defined by the following: d E ( P , Q ) = ∑ i n ( p i − q i ) 2 . (16)
• Bhattacharyya distance [ 104 , 105 ]: The Bhattacharyya distance is a statistical measure that quantifies the similarity between two discrete or continuous probability distributions. This distance is particularly known for its low processing time and its low sensitivity to noise. For the probability distributions p and q defined on the same domain, the distance of Bhattacharyya is defined as follows: D B ( p ,   q ) = − l n ( B C ( p ,   q ) ) , (17) B C ( p ,   q ) = ∑ x ∈ X p ( x ) q ( x )   ( a ) ;   B C ( p ,   q ) = ∫ p ( x ) q ( x ) d x   ( b ) , (18) where B C is the Bhattacharyya coefficient, defined as Equation (18a) for discrete probability distributions and as Equation (18b) for continuous probability distributions. In both cases, 0 ≤ BC ≤ 1 and 0 ≤ DB ≤ ∞. In its simplest formulation, the Bhattacharyya distance between two classes that follow a normal distribution can be calculated from a mean ( μ ) and the variance ( σ 2 ): D B ( p ,   q ) = 1 4 l n ( 1 4 ( σ p 2 σ q 2 + σ q 2 σ p 2 + 2 ) ) + 1 4 ( ( μ p − μ q ) σ q 2 + σ p 2 ) . (19)
• Chi-squared distance [ 106 ]: The Chi-squared ( X 2 ) distance was weighted by the value of the samples, which allows knowing the same relevance for sample differences with few occurrences as those with multiple occurrences. To compare two histograms S 1 = ( u 1 , …   …   … . u m ) and S 2 = ( w 1 , …   …   … . w m ) , the Chi-squared ( X 2 ) distance can be defined as follows: ( X 2 ) = D ( S 1 , S 2 ) = 1 2 ∑ i = 1 m ( u i − w i ) 2 u i + w i . (20)

6.2. Classifiers

• Support vector machines (SVMs) [ 13 , 26 ]: The feature vectors extracted by any descriptor are classified by linear or nonlinear SVM. The SVM classifier may realize the separation of the classes with an optimal hyperplane. To determine the last, only the closest points of the total learning set should be used; these points are called support vectors ( Figure 14 ). There is an infinite number of hyperplanes capable of perfectly separating two classes, which implies to select a hyperplane that maximizes the minimal distance between the learning examples and the learning hyperplane (i.e., the distance between the support vectors and the hyperplane). This distance is called “margin”. The SVM classifier is used to calculate the optimal hyperplane that categorizes a set of labels training data in the correct class. The optimal hyperplane is solved as follows: D = { ( x i , y i ) | x i ∈ R n ,   y i ∈ { − 1 , 1 } ,   i = 1 … … l } . (21) Given that x i are the training features vectors and y i are the corresponding set of l (1 or −1) labels. An SVM tries to find a hyperplane to distinguish the samples with the smallest errors. The classification function is obtained by calculating the distance between the input vector and the hyperplane. w x i − b = C f , (22) where w and b are the parameters of the model. Shen et al. [ 108 ] proposed the Gabor filter to extract the face features and applied the SVM for classification. The proposed FaceNet method achieves a good record accuracy of 99.63% and 95.12% using the LFW YouTube Faces DB datasets, respectively.
• k-nearest neighbor (k-NN) [ 17 , 91 ]: k-NN is an indolent algorithm because, in training, it saves little information, and thus does not build models of difference, for example, decision trees.
• K-means [ 9 , 109 ]: It is called K-means because it represents each of the groups by the average (or weighted average) of its points, called the centroid. In the K-means algorithm, it is necessary to specify a priori the number of clusters k that one wishes to form in order to start the process.
• Deep learning (DL): An automatic learning technique that uses neural network architectures. The term “deep” refers to the number of hidden layers in the neural network. While conventional neural networks have one layer, deep neural networks (DNN) contain several layers, as presented in Figure 15 .
• Convolutional layer : sometimes called the feature extractor layer because features of the image are extracted within this layer. Convolution preserves the spatial relationship between pixels by learning image features using small squares of the input image. The input image is convoluted by employing a set of learnable neurons. This produces a feature map or activation map in the output image, after which the feature maps are fed as input data to the next convolutional layer. The convolutional layer also contains rectified linear unit (ReLU) activation to convert all negative value to zero. This makes it very computationally efficient, as few neurons are activated each time.
• Pooling layer: used to reduce dimensions, with the aim of reducing processing times by retaining the most important information after convolution. This layer basically reduces the number of parameters and computation in the network, controlling over fitting by progressively reducing the spatial size of the network. There are two operations in this layer: average pooling and maximum pooling: - Average-pooling takes all the elements of the sub-matrix, calculates their average, and stores the value in the output matrix. - Max-pooling searches for the highest value found in the sub-matrix and saves it in the output matrix.
• Fully-connected layer : in this layer, the neurons have a complete connection to all the activations from the previous layers. It connects neurons in one layer to neurons in another layer. It is used to classify images between different categories by training.

6.3. Databases Used

• LFW (Labeled Faces in the Wild) database was created in October 2007. It contains 13,333 images of 5749 subjects, with 1680 subjects with at least two images and the rest with a single image. These face images were taken on the Internet, pre-processed, and localized by the Viola–Jones detector with a resolution of 250 × 250 pixels. Most of them are in color, although there are also some in grayscale and presented in JPG format and organized by folders.
• FERET (Face Recognition Technology) database was created in 15 sessions in a semi-controlled environment between August 1993 and July 1996. It contains 1564 sets of images, with a total of 14,126 images. The duplicate series belong to subjects already present in the series of individual images, which were generally captured one day apart. Some images taken from the same subject vary overtime for a few years and can be used to treat facial changes that appear over time. The images have a depth of 24 bits, RGB, so they are color images, with a resolution of 512 × 768 pixels.
• AR face database was created by Aleix Martínez and Robert Benavente in the computer vision center (CVC) of the Autonomous University of Barcelona in June 1998. It contains more than 4000 images of 126 subjects, including 70 men and 56 women. They were taken at the CVC under a controlled environment. The images were taken frontally to the subjects, with different facial expressions and three different lighting conditions, as well as several accessories: scarves, glasses, or sunglasses. Two imaging sessions were performed with the same subjects, 14 days apart. These images are a resolution of 576 × 768 pixels and a depth of 24 bits, under the RGB RAW format.
• ORL Database of Faces was performed between April 1992 and April 1994 at the AT & T laboratory in Cambridge. It consists of a total of 10 images per subject, out of a total of 40 images. For some subjects, the images were taken at different times, with varying illumination and facial expressions: eyes open/closed, smiling/without a smile, as well as with or without glasses. The images were taken under a black homogeneous background, in a vertical position and frontally to the subject, with some small rotation. These are images with a resolution of 92 × 112 pixels in grayscale.
• Extended Yale Face B database contains 16,128 images of 640 × 480 grayscale of 28 individuals under 9 poses and 64 different lighting conditions. It also includes a set of images made with the face of individuals only.
• Pointing Head Pose Image Database (PHPID) is one of the most widely used for face recognition. It contains 2790 monocular face images of 15 persons with tilt angles from −90° to +90° and variations of pan. Every person has two series of 93 different poses (93 images). The face images were taken under different skin color and with or without glasses.

6.4. Comparison between Holistic, Local, and Hybrid Techniques

7. discussion about future directions and conclusions, 7.1. discussion.

• Local approaches: use features in which the face described partially. For example, some system could consist of extracting local features such as the eyes, mouth, and nose. The features’ values are calculated from the lines or points that can be represented on the face image for the recognition step.
• Holistic approaches: use features that globally describe the complete face as a model, including the background (although it is desirable to occupy the smallest possible surface).
• Hybrid approaches: combine local and holistic approaches.
• Three-dimensional face recognition: In 2D image-based techniques, some features are lost owing to the 3D structure of the face. Lighting and pose variations are two major unresolved problems of 2D face recognition. Recently, 3D facial recognition for facial recognition has been widely studied by the scientific community to overcome unresolved problems in 2D facial recognition and to achieve significantly higher accuracy by measuring geometry of rigid features on the face. For this reason, several recent systems based on 3D data have been developed [ 3 , 93 , 95 , 128 , 129 ].
• Multimodal facial recognition: sensors have been developed in recent years with a proven ability to acquire not only two-dimensional texture information, but also facial shape, that is, three-dimensional information. For this reason, some recent studies have merged the two types of 2D and 3D information to take advantage of each of them and obtain a hybrid system that improves the recognition as the only modality [ 98 ].
• Deep learning (DL): a very broad concept, which means that it has no exact definition, but studies [ 14 , 110 , 111 , 112 , 113 , 121 , 130 , 131 ] agree that DL includes a set of algorithms that attempt to model high level abstractions, by modeling multiple processing layers. This field of research began in the 1980s and is a branch of automatic learning where algorithms are used in the formation of deep neural networks (DNN) to achieve greater accuracy than other classical techniques. In recent progress, a point has been reached where DL performs better than people in some tasks, for example, to recognize objects in images.

7.2. Conclusions

Author contributions, conflicts of interest.

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Kortli, Y.; Jridi, M.; Al Falou, A.; Atri, M. Face Recognition Systems: A Survey. Sensors 2020 , 20 , 342. https://doi.org/10.3390/s20020342

Kortli Y, Jridi M, Al Falou A, Atri M. Face Recognition Systems: A Survey. Sensors . 2020; 20(2):342. https://doi.org/10.3390/s20020342

Kortli, Yassin, Maher Jridi, Ayman Al Falou, and Mohamed Atri. 2020. "Face Recognition Systems: A Survey" Sensors 20, no. 2: 342. https://doi.org/10.3390/s20020342

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Face Recognition Thesis

Face recognition is one of the thriving technologies which effectively analysis images and videos to recognize faces. It is presented in every technology to identify the users in order to ensure the correct access to the intended accounts. Smart mobile phones are the greatest example of face recognition . The former decade’s researches have significantly contributed their part to developing face-recognizing techniques. “In this article, we have clearly stated about the things to be considered before writing a face recognition thesis ”

In the earlier stages, techniques such as pattern recognition & computer vision are tried their best to offer consistent face recognition results by detecting and tracking the faces . In fact, now systems are developed well to perform all the required processes regarding face recognition. As the digital world is subject to security and privacy matters, it is demanding identity recognitions hence face-recognizing systems contributing their superlative features.

At the end of this article, you can able to write your own face recognition thesis . In point of fact, feature extraction is one of the important procedures of face recognition. In other words, it is the process of extracting the necessary data from a given image or video . These data can be used for the identification or verification processes of the subjects with reduced error rates.

Further, Evaluating time/duration & memory consumptions are determining the efficiency of the face recognition processes . In addition, outcomes of the face recognition are dealt with the optimization for the further classification phases. Now we can have the section about how the face recognition procedure works in real-time with clear handy notes.  

How Face Recognition Works?

The face recognition system takes input in the form of image or video frames and verifies or identifies the subjects presented in the inputs. For your better understanding, we have vibrantly itemized the steps that are commonly used to recognize the faces of human beings.

• Face Recognition Inputs
• Face (Points) Detection
• Facial Feature Extraction
• Face Recognition
• Facial Emotion Recognition

The aforesaid are the major steps involved in recognizing the human faces besides there are several approaches that are often used to achieve the predetermined aspects. In that, some of the significant approaches practiced in every step are mentioned for making your understanding as strong.

• Image Compression
• Posture Analysis
• Face Tracing
• Gaze Evaluation
• Sentiment / Emotion Recognition
• Feature Geometries
• Holistic Template
• Hybrid Approaches

The final stage of face recognition can be done with the help of above listed other steps. In the point of fact, verification (authentication) & identification processes are pillared by these steps. There is a database that is stored with a massive amount of images regarding human faces.

If an image or video is given into the face recognition processes, the system will perform the one-to-many matching and will get the identity report from the database. On the other hand,  the identification  process   is done even if the input is unknown (unidentified), the system would match the individuals with the known individuals (identified) presented in that database. 

In addition,  the verification  process is performed according to the one-to-one face matching tasks. Here the unknown users are authenticated by the identity claims. This is how face recognition works in general. Moreover, it is also vital to consider the accuracy of the face-recognizing systems . Reach us for more interesting face recognition thesis topics. Here, one of the major factors is illustrated that is affecting the face recognition accuracy levels for your better understanding.   

Factors Affecting Face Recognition Accuracy 

The accuracy of face recognition highly depends upon the  selection of the FRT  (Face Recognition Technology) techniques/algorithms. Those algorithms are explained below.

• Focuses on the facial features like eyes, nose & mouth, etc.
• Appearance algorithms never consider the facial feature reflection & postures
• Hybrid is the combination of feature & appearance Face Recognition algorithms
• It is intended and proposed to diminish the false rates in recognition

In short, the selection of the Face Recognition algorithm is very important to achieve the great accuracy levels of face recognition. If you are trying your researches in face recognition areas consider handpicking the Face Recognition techniques . If it is failed there might be a lot of chances to face issues. In this regard, let’s discuss the key issues that are presented in the face recognition systems in general for your better understanding.

Key Issues in Face Recognition

• Illumination / Lighting
• Facial Expression / Manifestation
• Facial Feature Occlusion / Obstruction
• Artifacts in Images
• Posture Variations / Dissimilarities
• Image Rotation / Spinning

These listed are some of the main issues that are aroused in facial recognition. In point of fact, facial recognition is a little complex in nature because in recent days high-resolution cameras are having the capacity to renovate the facial feature with the help of their inbuilt features. In addition to that quality of the face, recognition falls under a question mark . In the subsequent passage, we have mentioned to you how we can enhance the facial recognition processes.

How Can Facial Recognition be improved?

Facial recognition processes can be improved according to the quality of images/videos acquired. The quality of the images is manipulated by the factors such as illumination (lighting), occlusion (obstruction) & postures/gestures of the individuals . In fact, these can be achieved with the application of several approaches as mentioned in the upcoming section.

• Representation of Components
• Clustered Structure & Component Representation
• Global Level Representation
• Local Supporting Features
• Global Supporting Features
• Manual/Handcrafted Extraction
• Image Texture
• Face Shapes
• Learning oriented Extraction
• Decision Trees
• Dictionaries
• Deep Neural Network
• Model oriented Extraction
• Graph & Shape
• Appearance oriented Extraction
• Multi-Linear
• Liner & Non-Linear

Afore listed are the various approaches that are supporting attain the improvements in facial recognition. Our researchers in the concern are well expert in the approaches of face recognition technology. This is becoming possible by conducting so many concurrent pieces of research in every single area of technology.

So the capacities of our engineers are getting multiplicities in various aspects and perceptions. In addition to this area, let us also discuss the classical face recognition algorithms and methods with their pros and cons for the ease of your understanding.  

Classified Face Recognition Algorithms & Methods

• Redundancies Application
• Image Enrichment
• Ineffective Analysis
• Lack of Determining Similarities
• High Accuracy Levels
• Resilient Outcomes
• Illumination-free & Posture-free
• Lack of 2D Image Compatibility
• Costly for Real-time Applications
• Discriminant Feature Learning
• Minimizes & Maximizes Differences (images)
• Gels with Low Lighting & Expression Variations
• Simplified Descriptor Extraction
• Lack of Intensity
• Effective Frequency Features
• Diverse Biometric Application
• Impractical High Dimensions
• Sensitive in Illumination Changes
• Simplified Linear Processes
• Speed Computations
• Compatible with Occlusion & Incomplete Data
• Integration of Negative-free Matrix & Radial Basis
• Huge Training Models
• Inaccuracy Results
• Adaptive with Diverse Structures
• Linear Subspace Projections
• Great Mahalanobis Distances
• Delicate in Lighting Variations
• Ambiguity in Neighborhood Selection

The itemized above are the major classical methods used in face recognition . In fact, face recognition techniques are full-fledged in identifying the features and preprocessing the same. Further face regions are extracted and backgrounds of the images are segmented.

A separated image is refined under the processes of contrast detection and feature extraction. Facial point detection (nose, mouth, eyes) helps to accurately identify the individuals. By the way, at this time it will be really helpful to know about the recent face recognition project ideas .   

Recent Face Recognition Thesis Ideas

• 3D & Multi-Dimension based Emotion Recognition
• Dynamic Facial Posture Detection
• Face Depression/Sadness Recognition
• Multi-Level Face Recognition & Clustering
• Anticipated Spoofing & 3D Face Mask Authentication
• 3D Face Arrangement & Displaying
• Multi-Level Face Tracing & Grouping
• Micro & Dynamic Facial Expression Recognition

The above listed are some of the interesting face recognition thesis ideas , you can also select one of the mentioned ideas to develop your own thesis by considering these ideas as a reference. Here, our researchers of the institute have planned to exhibit the evaluation of the face recognition performance . Come on guys let us try to understand them.

How to Estimate the Performance of Face Recognition?

• ‘Yes’ and ‘no’ are the two binary decisions in facial verification processes
• ‘Yes’ signifies similar individuals & ‘no’ signifies the different individuals
• Errors categorization are done in the verification processes
• These two binary decisions always give the output in the 4 aspects as,
• False-negative:  Identification of a single individual in 2 unlike persons
• False-positive:  Shows the 2 dissimilar persons by way of the same person
• True negative:  Exactly identifies the   2 dissimilar persons in given images
• True positive: Identification of identical person is given 2 images

These are some of the important aspects that are needed to be considered while evaluating the performance of face recognition . Thesis writing is one of the major writing works that is required in the master’s degree and PhD academic levels . In fact, thesis writing is nearly similar to the research papers that are proposed in the research areas.

As this article is focused on giving content related to the face recognition thesis we are actually going to state the ways to write a good quality thesis for the ease of your understanding in the subsequent section.

What is the Way to Write a Good Quality Thesis?

• Gather the previous researches and give your “peculiar examination/analysis” on the “findings”
• Exhibit your “analytical & critical thinking abilities” and show the “interesting fields” in your thesis
• Refer to the “earlier researches” to completely discover your own “newfangled   propositions”

On the other hand, it is very important to deliver the findings in a structural manner. As the thesis is the experimental study of the proposed technical areas it is to be presented in an organized way. Actually, we have listed the things that are contained in every thesis for the ease of your understanding in the immediate phase.

• Introduction or background of the research
• Literature reviews & related works
• Research problem findings
• Methodologies & questions
• Discussions on outcomes
• References & final conclusions

The above listed are the ways of constructing a good thesis as well as the contents involved in the structure of every thesis . IN fact, we do encourage the students to act like this as well as help them to attain the levels predetermined. Actually, students from all over the world are being benefited by our services rendered. For your valuable consideration, a pinch of our working style in the areas of dissertation writing has been enumerated in the next section.  

How Do We Write a Thesis?

• Structuring thesis formats in the referencing panaches like Chicago, Turabian, APA, & MLA
• Framing the contents with the newfangled ideas & with plagiarism-free
• Delivering both virtual & offline dissertation writing assistances with privacy
• Customizing thesis contents as per requirements with supreme quality

This is how we work on thesis writing in fact, this is just a sample of our working manner. Actually, we do have so many interesting fields and assistances for the students of every institution. So far, we have come up with the concepts that are requisites for framing the effective face recognition thesis.   Moreover, we are also expecting you guys to explore more in these areas of technology. If you are really interested then you can approach our technical team at any time and the high-quality thesis guidance is waiting for you. Let’s hurry to avail our services.

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PhD Projects in Face Recognition

Face Recognition is a  “ strong security-aware area”  in the DIP field.  PhD Projects in Face Recognition  is our wide research service. As a matter of fact, it consists of “erudite environs” for PhD/MS scholars. It happens due to the ever-rising need in the defense system.

At first, it captures one’s bio proof of identity as facial attributes. Then, it analyzes their patterns to detect the original self. At last, it yields an exact outcome as per your needs.

Up-to-date applications of Face Recognition

• Forensic Detection System
• Biometric Personal Authentication
• Video Surveillance System
• Residential Security System
• Access Control System
• Healthcare Monitoring System
• E-Transaction in Online Shopping
• Bank ATM Hubs

Bio-Inspired Optimization Algorithms

• Artificial Bee Colony Algorithm
• Glowworm Swarm Optimization
• Invasive Weed Optimization
• Bumble Bees Mating Optimization
• Bacterial Colony Optimization
• Social Spider Optimization
• Spider Monkey Optimization
• Ant Lion Optimization Algorithm

To be sure, we are at ease with, above all, face recognition algorithms. Not only in this, but also we are skilled to work on other algorithms ( ML, DL, CNN, and so on ). In any case, we are ready to  “develop a new algorithm”  too.

All in all, we assist you in each aspect of your research trip. In the end, we will deliver your work with  “good quality”  in a short time. On the whole,  PhD projects in Face Recognition  are avid to do all of the real-time projects.

An effective mechanism for Multi-Faces Recognition Process used by Haar Cascades and Eigenface Methods

An innovative function of F-DR Net Face detection and recognition in One Net system

A new process for Wasserstein CNN by Learning Invariant Features intended for NIR-VIS Face Recognition method

An inventive source of Face Segmentation, Face Swapping, and Face Perception practice

The novel technique for Multi-scale feature extraction for single face recognition system

An innovative mechanism for Trunk-Branch Ensemble Convolutional Neural Networks used Video-Based Face Recognition

The novel mechanism for Adaptive Pose Alignment in Pose-Invariant Face Recognition

A novel method for Face Recognition Based on Stacked Convolutional Autoencoder and Sparse Representation system

A new- fangled mechanism for Face Recognition Based on PCA with Weighted and Normalized Mahalanobis distance practice

A novel approach for Face Feature Dynamic Recognition Method Based on Intelligent Image scheme

An effective Performance for Blur and Motion Blur Influence based on Face Recognition system

An effective Performance Evaluation of Software for Face Recognition, Based on Dlib and Opencv Library

The novel mechanism for B-Face on CNN-Based Face Recognition Processor with Face Alignment for Mobile User Identification system

Design and develop Application of Statistical Data Processing intended for Solving Problem of Face Recognition via Principal Components Analysis Method

An innovative performance for Experiments based on Deep Face Recognition by Partial Faces system

The novel methodology for Automatic System for Smile Recognition Based on CNN and Face Detection

Fresh mechanism for LBPH based on improved face recognition at low resolution scheme

A new-fangled mechanism for Fast Face Recognition System Based on Deep Learning

An effective function of Face Recognition used by DRLBP and SIFT Feature Extraction

The new source of  Multi-Face Challenging Dataset designed for Robust Face Recognition

MILESTONE 1: Research Proposal

Finalize journal (indexing).

Before sit down to research proposal writing, we need to decide exact journals. For e.g. SCI, SCI-E, ISI, SCOPUS.

Research Subject Selection

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Research Topic Selection

We helping you with right and perfect topic selection, which sound interesting to the other fellows of your committee. For e.g. if your interest in networking, the research topic is VANET / MANET / any other

Literature Survey Writing

To ensure the novelty of research, we find research gaps in 50+ latest benchmark papers (IEEE, Springer, Elsevier, MDPI, Hindawi, etc.)

Case Study Writing

After literature survey, we get the main issue/problem that your research topic will aim to resolve and elegant writing support to identify relevance of the issue.

Problem Statement

Based on the research gaps finding and importance of your research, we conclude the appropriate and specific problem statement.

Writing Research Proposal

Writing a good research proposal has need of lot of time. We only span a few to cover all major aspects (reference papers collection, deficiency finding, drawing system architecture, highlights novelty)

MILESTONE 2: System Development

Fix implementation plan.

We prepare a clear project implementation plan that narrates your proposal in step-by step and it contains Software and OS specification. We recommend you very suitable tools/software that fit for your concept.

Tools/Plan Approval

We get the approval for implementation tool, software, programing language and finally implementation plan to start development process.

Pseudocode Description

Our source code is original since we write the code after pseudocodes, algorithm writing and mathematical equation derivations.

Develop Proposal Idea

We implement our novel idea in step-by-step process that given in implementation plan. We can help scholars in implementation.

Comparison/Experiments

We perform the comparison between proposed and existing schemes in both quantitative and qualitative manner since it is most crucial part of any journal paper.

Graphs, Results, Analysis Table

We evaluate and analyze the project results by plotting graphs, numerical results computation, and broader discussion of quantitative results in table.

Project Deliverables

For every project order, we deliver the following: reference papers, source codes screenshots, project video, installation and running procedures.

MILESTONE 3: Paper Writing

Choosing right format.

We intend to write a paper in customized layout. If you are interesting in any specific journal, we ready to support you. Otherwise we prepare in IEEE transaction level.

Collecting Reliable Resources

Before paper writing, we collect reliable resources such as 50+ journal papers, magazines, news, encyclopedia (books), benchmark datasets, and online resources.

Writing Rough Draft

We create an outline of a paper at first and then writing under each heading and sub-headings. It consists of novel idea and resources

Proofreading & Formatting

We must proofread and formatting a paper to fix typesetting errors, and avoiding misspelled words, misplaced punctuation marks, and so on

Native English Writing

We check the communication of a paper by rewriting with native English writers who accomplish their English literature in University of Oxford.

Scrutinizing Paper Quality

We examine the paper quality by top-experts who can easily fix the issues in journal paper writing and also confirm the level of journal paper (SCI, Scopus or Normal).

Plagiarism Checking

We at phdservices.org is 100% guarantee for original journal paper writing. We never use previously published works.

MILESTONE 4: Paper Publication

Finding apt journal.

We play crucial role in this step since this is very important for scholar’s future. Our experts will help you in choosing high Impact Factor (SJR) journals for publishing.

Lay Paper to Submit

We organize your paper for journal submission, which covers the preparation of Authors Biography, Cover Letter, Highlights of Novelty, and Suggested Reviewers.

Paper Submission

We upload paper with submit all prerequisites that are required in journal. We completely remove frustration in paper publishing.

Paper Status Tracking

We track your paper status and answering the questions raise before review process and also we giving you frequent updates for your paper received from journal.

Revising Paper Precisely

When we receive decision for revising paper, we get ready to prepare the point-point response to address all reviewers query and resubmit it to catch final acceptance.

Get Accept & e-Proofing

We receive final mail for acceptance confirmation letter and editors send e-proofing and licensing to ensure the originality.

Publishing Paper

Paper published in online and we inform you with paper title, authors information, journal name volume, issue number, page number, and DOI link

MILESTONE 5: Thesis Writing

Identifying university format.

We pay special attention for your thesis writing and our 100+ thesis writers are proficient and clear in writing thesis for all university formats.

Gathering Adequate Resources

We collect primary and adequate resources for writing well-structured thesis using published research articles, 150+ reputed reference papers, writing plan, and so on.

Writing Thesis (Preliminary)

We write thesis in chapter-by-chapter without any empirical mistakes and we completely provide plagiarism-free thesis.

Skimming & Reading

Skimming involve reading the thesis and looking abstract, conclusions, sections, & sub-sections, paragraphs, sentences & words and writing thesis chorological order of papers.

Fixing Crosscutting Issues

This step is tricky when write thesis by amateurs. Proofreading and formatting is made by our world class thesis writers who avoid verbose, and brainstorming for significant writing.

Organize Thesis Chapters

We organize thesis chapters by completing the following: elaborate chapter, structuring chapters, flow of writing, citations correction, etc.

Writing Thesis (Final Version)

We attention to details of importance of thesis contribution, well-illustrated literature review, sharp and broad results and discussion and relevant applications study.

How PhDservices.org deal with significant issues ?

1. novel ideas.

Novelty is essential for a PhD degree. Our experts are bringing quality of being novel ideas in the particular research area. It can be only determined by after thorough literature search (state-of-the-art works published in IEEE, Springer, Elsevier, ACM, ScienceDirect, Inderscience, and so on). SCI and SCOPUS journals reviewers and editors will always demand “Novelty” for each publishing work. Our experts have in-depth knowledge in all major and sub-research fields to introduce New Methods and Ideas. MAKING NOVEL IDEAS IS THE ONLY WAY OF WINNING PHD.

2. Plagiarism-Free

To improve the quality and originality of works, we are strictly avoiding plagiarism since plagiarism is not allowed and acceptable for any type journals (SCI, SCI-E, or Scopus) in editorial and reviewer point of view. We have software named as “Anti-Plagiarism Software” that examines the similarity score for documents with good accuracy. We consist of various plagiarism tools like Viper, Turnitin, Students and scholars can get your work in Zero Tolerance to Plagiarism. DONT WORRY ABOUT PHD, WE WILL TAKE CARE OF EVERYTHING.

3. Confidential Info

We intended to keep your personal and technical information in secret and it is a basic worry for all scholars.

• Technical Info: We never share your technical details to any other scholar since we know the importance of time and resources that are giving us by scholars.
• Personal Info: We restricted to access scholars personal details by our experts. Our organization leading team will have your basic and necessary info for scholars.

CONFIDENTIALITY AND PRIVACY OF INFORMATION HELD IS OF VITAL IMPORTANCE AT PHDSERVICES.ORG. WE HONEST FOR ALL CUSTOMERS.

4. Publication

Most of the PhD consultancy services will end their services in Paper Writing, but our PhDservices.org is different from others by giving guarantee for both paper writing and publication in reputed journals. With our 18+ year of experience in delivering PhD services, we meet all requirements of journals (reviewers, editors, and editor-in-chief) for rapid publications. From the beginning of paper writing, we lay our smart works. PUBLICATION IS A ROOT FOR PHD DEGREE. WE LIKE A FRUIT FOR GIVING SWEET FEELING FOR ALL SCHOLARS.

5. No Duplication

After completion of your work, it does not available in our library i.e. we erased after completion of your PhD work so we avoid of giving duplicate contents for scholars. This step makes our experts to bringing new ideas, applications, methodologies and algorithms. Our work is more standard, quality and universal. Everything we make it as a new for all scholars. INNOVATION IS THE ABILITY TO SEE THE ORIGINALITY. EXPLORATION IS OUR ENGINE THAT DRIVES INNOVATION SO LET’S ALL GO EXPLORING.

Client Reviews

I ordered a research proposal in the research area of Wireless Communications and it was as very good as I can catch it.

I had wishes to complete implementation using latest software/tools and I had no idea of where to order it. My friend suggested this place and it delivers what I expect.

It really good platform to get all PhD services and I have used it many times because of reasonable price, best customer services, and high quality.

My colleague recommended this service to me and I’m delighted their services. They guide me a lot and given worthy contents for my research paper.

I’m never disappointed at any kind of service. Till I’m work with professional writers and getting lot of opportunities.

- Christopher

Once I am entered this organization I was just felt relax because lots of my colleagues and family relations were suggested to use this service and I received best thesis writing.

I recommend phdservices.org. They have professional writers for all type of writing (proposal, paper, thesis, assignment) support at affordable price.

You guys did a great job saved more money and time. I will keep working with you and I recommend to others also.

These experts are fast, knowledgeable, and dedicated to work under a short deadline. I had get good conference paper in short span.

Guys! You are the great and real experts for paper writing since it exactly matches with my demand. I will approach again.

I am fully satisfied with thesis writing. Thank you for your faultless service and soon I come back again.

Trusted customer service that you offer for me. I don’t have any cons to say.

I was at the edge of my doctorate graduation since my thesis is totally unconnected chapters. You people did a magic and I get my complete thesis!!!

- Abdul Mohammed

Good family environment with collaboration, and lot of hardworking team who actually share their knowledge by offering PhD Services.

I enjoyed huge when working with PhD services. I was asked several questions about my system development and I had wondered of smooth, dedication and caring.

I had not provided any specific requirements for my proposal work, but you guys are very awesome because I’m received proper proposal. Thank you!

- Bhanuprasad

I was read my entire research proposal and I liked concept suits for my research issues. Thank you so much for your efforts.

- Ghulam Nabi

I am extremely happy with your project development support and source codes are easily understanding and executed.

Hi!!! You guys supported me a lot. Thank you and I am 100% satisfied with publication service.

- Abhimanyu

I had found this as a wonderful platform for scholars so I highly recommend this service to all. I ordered thesis proposal and they covered everything. Thank you so much!!!

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