Face Recognition Systems: A Survey
Abstract
: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, preprocessing 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], regionofinterest 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], scaleinvariant 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 knearest neighbor (KNN) [40] are known to effectively address this task.
2.2. Classification of Face Recognition Systems
3. Local Approaches
3.1. Local AppearanceBased 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 $3\times 3$ pixel matrix $(\mathrm{p}1\dots \dots \mathrm{p}8$) is mapped across the square. The pixel of this matrix is a threshold with the value of the center pixel $({\mathrm{p}}_{0})$ (i.e., use the intensity value of the center pixel $\mathrm{i}\left({\mathrm{p}}_{0}\right)$ 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).$$\mathrm{LBP}={\displaystyle \sum}_{p=1}^{8}{2}^{p}s\left({i}_{0}{i}_{p}\right),\text{}with\text{}s\left(x\right)=\{\begin{array}{cc}1& x\ge 0\\ 0& x0\end{array},$$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 (RILBP). Xi et al. [15] have introduced a new unsupervised deep learningbased 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 KNN 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 firstorder gradients of each cell in both the horizontal and vertical direction. The most common method is to apply a 1D mask, [–1 0 1].$${G}_{x}\left(x,\text{}y\right)=I\left(x+1,\text{}y\right)I\left(x1,\text{}y\right),$$$${G}_{y}\left(x,\text{}y\right)=I\left(x,\text{}y+1\right)I\left(x,\text{}y1\right),$$$$G\left(x,\text{}y\right)=\sqrt{{G}_{x}{\left(x,\text{}y\right)}^{2}+{G}_{y}{\left(x,\text{}y\right)}^{2}},$$$$\theta \left(x,\text{}y\right)={\mathrm{tan}}^{1}\left(\frac{{G}_{y}\left(x,\text{}y\right)}{{G}_{x}\left(x,\text{}y\right)}\right).$$The magnitude of the gradient and the orientation of each pixel in the cell are voted in nine bins with the trilinear 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 “multiHOG”.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, shiftinvariance, 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 “$4f$ ” 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 $\mathrm{P}$ is applied (for example, the phaseonly filter (POF) filter [2]) using optoelectronic interfaces. Finally, to obtain the filtered face image ${F}^{\prime}$ (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 2DFFT 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 2DFFT 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.$$C=FF{T}^{1}\left\{{S}^{\ast}\circ POF\right\},$$$${H}_{POF}\left(u,v\right)=\frac{{S}^{\ast}\left(u,v\right)}{\leftS\left(u,v\right)\right},$$$$PCE=\frac{{\sum}_{i,j}^{N}{E}_{peak}\left(i,j\right)}{{\sum}_{i,j}^{M}{E}_{correlationplane}\left(i,j\right)},$$
3.2. KeyPointsBased 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 realtime 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 spacescale, (2) location of characteristic points, (3) assignment of orientation, and (4) a descriptor of the characteristic point. A framework to detect the keypoints 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.
 Speededup 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 $M\times N$, so that a typical image of size 4 × 4 becomes a vector of dimension 16. Let the training set of images be $\left\{{X}_{1},{X}_{2},\text{}{X}_{3}\dots \text{}{X}_{N}\right\}$. The average face of the set is defined by the following:$$\overline{X}=\frac{1}{N}{\displaystyle \sum}_{i=1}^{N}X{\text{}}_{i}.$$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:$$Q=\frac{1}{N}{\displaystyle \sum}_{i=1}^{N}\left(\overline{X}{X}_{i}\right){\left(\overline{X}{X}_{i}\right)}^{\mathrm{T}}.$$The Eigenvectors and corresponding Eigenvalues are computed using$$CV=\lambda V,\text{}\left(V\u03f5{R}_{n},\text{}V\ne 0\right),$$$${y}_{k}^{i}={w}^{T}\text{}\left({x}_{i}\right),\text{}(i=1,\text{}2,\text{}3\text{}\dots \text{}N),$$
 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 withinclass scatter matrix ${S}_{W}$ and the betweenclass scatter matrix ${S}_{B}$ are defined as follows:$${S}_{B}={{\displaystyle \sum}}_{I=1}^{C}{M}_{i}\left({x}_{i}\mu \right){\left({x}_{i}\mu \right)}^{T},$$$${S}_{w}={{\displaystyle \sum}}_{I=1}^{C}{\displaystyle \sum}_{{x}_{k}\u03f5{X}_{i}}{M}_{i}\left({x}_{k}\mu \right){\left({x}_{k}\mu \right)}^{T},$$
 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 tokenfrequency (TF) features from each region by sumpooling 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 lowfrequency 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 twodimensional discrete wavelet transform (2DDWT) method for face recognition using a new patch strategy. A nonuniform patch strategy for the toplevel’s lowfrequency subband is proposed by using an integral projection technique for two toplevel highfrequency subbands of 2DDWT 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.
Algorithm 1. DCT Algorithm 

4.2. Nonlinear Techniques
 Kernel PCA (KPCA) [28]: is an improved method of PCA, which uses kernel method techniques. KPCA computes the Eigenfaces or the Eigenvectors of the kernel matrix, while PCA computes the covariance matrix. In addition, KPCA is a representation of the PCA technique on the highdimensional feature space mapped by the associated kernel function. Three significant steps of the KPCA algorithm are used to calculates the function of the kernel matrix $\mathrm{K}$ of distribution consisting of $\mathrm{n}$ data points ${\mathrm{x}}_{\mathrm{i}}\in {\mathrm{R}}^{\mathrm{d}}$, after which the data points are mapped into a highdimensional feature space $\mathrm{F}$, as shown in Algorithm 2.
Algorithm 2. Kernel PCA Algorithm  Step 1: Determine the dot product of the matrix$K$using kernel function:${K}_{ij}=k\left({x}_{i},{x}_{j}\right)$.
 Step 2: Calculate the Eigenvectors from the resultant matrix$K$and normalize with the function:$\gamma k\left(\alpha k\alpha k\right)=1$.
 Step 3: Calculate the test point projection on to Eigenvectors$Vk$using kernel function:$kPCk\left(x\right)=\left(Vk\phi \left(x\right)\right)={\sum}_{i}^{m}\alpha {k}_{i}\text{}k\left({x}_{i},x\right)$
 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 classspecific kernel discriminant analysis classifier (CSKDA) based on the spectral regression kernel discriminant analysis. Other nonlinear techniques have also been used in the context of facial recognition:
 GaborKLDA [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 correlatorbased twolayer 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 (GWLDA) [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 2DLDA is used to maximize the interclass space and reduce the intraclass space. To classify and recognize the test face image, the knearest neighbour (kNN) 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.
 Overcomplete LBP (OCLBP), LDA, and within class covariance normalization (WCCN): Barkan et al. [92] proposed a new representation of face image based overcomplete LBP (OCLBP). This representation is a multiscale 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 singlesample periocularbased alignmentrobust face recognition technique based on highdimensional 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 stateoftheart algorithms under four evaluation protocols with a high accuracy of 89.69%.
 Multisubregionbased correlation filter bank (MSCFB): Yan et al. [94] propose an effective feature extraction technique for robust face recognition, named multisubregionbased correlation filter bank (MSCFB). MSCFB extracts the local features independently for each face subregion. After that, the different face subregions 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 stateoftheart 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 autoencoder (SAE) techniques: Ding et al. [95] proposed multimodal deep face representation (MMDFR) 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 MMDFR framework has two steps: a CNNs technique is used to extract the features and a threelayer stacked autoencoder (SAE) technique is employed to compress the highdimensional deep feature into a compact face signature. The LFW database is used to evaluate the identification performance of MMDFR. The flowchart of the proposed MMDFR framework is shown in Figure 12.
 PCA and ANFIS: Sharma et al. [96] propose an efficient poseinvariant 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 subregions, 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 CMUPIE, 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
 Peaktocorrelation energy (PCE) or peaktosidelobe 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 $\mathrm{P}1$ and $\mathrm{P}2,$ with the coordinates $\left(x1,\text{}y1\right)$ and $\left(x2,\text{}y2\right),$ respectively, the calculation of the Euclidean distance between them would be as follows:$${d}_{E}\left(P1,\text{}P2\text{}\right)=\sqrt{{\left(x2x1\right)}^{2}+{\left(y2y1\right)}^{2}}.$$In general, the Euclidean distance between two points $P=\left(1,\text{}p2,\text{}\dots ,\text{}pn\right)$ and $Q=\left(q1,\text{}q2,\dots \text{},\text{}qn\right)$ in the ndimensional space would be defined by the following:$${d}_{E}\left(P,Q\right)=\sqrt{{{\displaystyle \sum}}_{i}^{n}{\left(piqi\right)}^{2}}.$$
 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:$${D}_{B}\left(p,\text{}q\right)=ln\left(BC\left(p,\text{}q\right)\right),$$$$BC\left(p,\text{}q\right)={\sum}_{x\in X}\sqrt{p\left(x\right)q\left(x\right)}\text{}\left(a\right);\text{}BC\left(p,\text{}q\right)=\int \sqrt{p\left(x\right)q\left(x\right)dx}\text{}\left(b\right),$$$$DB\left(p,\text{}q\right)=\frac{1}{4}ln\left(\frac{1}{4}\left(\frac{{\sigma}_{p}^{2}}{{\sigma}_{q}^{2}}+\frac{{\sigma}_{q}^{2}}{{\sigma}_{p}^{2}}+2\right)\right)+\frac{1}{4}\left(\frac{\left({\mu}_{p}{\mu}_{q}\right)}{{\sigma}_{q}^{2}+{\sigma}_{p}^{2}}\right).$$
 Chisquared distance [106]: The Chisquared $\left({X}^{2}\right)$ 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 ${S}_{1}=\left({u}_{1},\dots \text{}\dots \text{}\dots .{u}_{m}\right)$ and ${S}_{2}=\left({w}_{1},\dots \text{}\dots \text{}\dots .{w}_{m}\right)$, the Chisquared $\left({X}^{2}\right)$ distance can be defined as follows:$$\left({X}^{2}\right)=D\left({S}_{1},{S}_{2}\right)=\frac{1}{2}{\displaystyle \sum}_{i=1}^{m}\frac{{\left({u}_{i}{w}_{i}\right)}^{2}}{{u}_{i}+{w}_{i}}.$$
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:$$D=\left\{\left({x}_{i},{y}_{i}\right){x}_{i}\in {R}^{n},\text{}{y}_{i}\in \left\{1,1\right\},\text{}i=1\dots \dots l\right\}.$$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.$$w{x}_{i}b={C}_{f},$$
 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:
 
 Averagepooling takes all the elements of the submatrix, calculates their average, and stores the value in the output matrix.
 
 Maxpooling searches for the highest value found in the submatrix and saves it in the output matrix.
 Fullyconnected 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, preprocessed, 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 semicontrolled 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.
 Threedimensional face recognition: In 2D imagebased 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 twodimensional texture information, but also facial shape, that is, threedimensional 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
Funding
Conflicts of Interest
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Author/Technique Used  Database  Matching  Limitation  Advantage  Result  

Local AppearanceBased Techniques  
Khoi et al. [20]  LBP  TDF  MAP  Skewness in face image  Robust feature in fontal face  5% 
CF1999  13.03%  
LFW  90.95%  
Xi et al. [15]  LBPNet  FERET  Cosine similarity  Complexities of CNN  High recognition accuracy  97.80% 
LFW  94.04%  
Khoi et al. [20]  PLBP  TDF  MAP  Skewness in face image  Robust feature in fontal face  5.50% 
CF  9.70%  
LFW  91.97%  
Laure et al. [40]  LBP and KNN  LFW  KNN  Illumination conditions  Robust  85.71% 
CMUPIE  99.26%  
Bonnen et al. [42]  MRF and MLBP  AR (Scream)  Cosine similarity  Landmark extraction fails or is not ideal  Robust to changes in facial expression  86.10% 
FERET (Wearing sunglasses)  95%  
Ren et al. [43]  Relaxed LTP  CMUPIE  Chisquare distance  Noise level  Superior performance compared with LBP, LTP  95.75% 
Yale B  98.71%  
Hussain et al. [60]  LPQ  FERET/  Cosine similarity  Lot of discriminative information  Robust to illumination variations  99.20% 
LFW  75.30%  
Karaaba et al. [44]  HOG and MMD  FERET  MMD/MLPD  Low recognition accuracy  Aligning difficulties  68.59% 
LFW  23.49%  
Arigbabu et al. [46]  PHOG and SVM  LFW  SVM  Complexity and time of computation  Head pose variation  88.50% 
Leonard et al. [50]  VLC correlator  PHPID  ASPOF  The low number of the reference image used  Robustness to noise  92% 
Napoléon et al. [38]  LBP and VLC  YaleB  POF  Illumination  Rotation + Translation  98.40% 
YaleB Extended  95.80%  
Heflin et al. [54]  correlation filter  LFW/PHPID  PSR  Some preprocessing steps  More effort on the eye localization stage  39.48% 
Zhu et al. [55]  PCA–FCF  CMUPIE  Correlation filter  Use only linear method  Occlusioninsensitive  96.60% 
FRGC2.0  91.92%  
Seo et al. [27]  LARK + PCA  LFW  Cosine similarity  Face detection  Reducing computational complexity  78.90% 
Ghorbel et al. [61]  VLC + DoG  FERET  PCE  Low recognition rate  Robustness  81.51% 
Ghorbel et al. [61]  uLBP + DoG  FERET  chisquare distance  Robustness  Processing time  93.39% 
Ouerhani et al. [18]  VLC  PHPID  PCE  Power  Processing time  77% 
KeyPointsBased Techniques  
Lenc et al. [56]  SIFT  FERET  a posterior probability  Still far to be perfect  Sufficiently robust on lower quality real data  97.30% 
AR  95.80%  
LFW  98.04%  
Du et al. [29]  SURF  LFW  FLANN distance  Processing time  Robustness and distinctiveness  95.60% 
Vinay et al. [23]  SURF + SIFT  LFW  FLANN  Processing time  Robust in unconstrained scenarios  78.86% 
Face94  distance  96.67%  
Calonder et al. [30]  BRIEF  _  KNN  Low recognition rate  Low processing time  48% 
Author/Techniques Used  Databases  Matching  Limitation  Advantage  Result  

Linear Techniques  
Seo et al. [27]  LARK and PCA  LFW  L2 distance  Detection accuracy  Reducing computational complexity  85.10% 
Annalakshmi et al. [35]  ICA and LDA  LFW  Bayesian Classifier  Sensitivity  Good accuracy  88% 
Annalakshmi et al. [35]  PCA and LDA  LFW  Bayesian Classifier  Sensitivity  Specificity  59% 
Hussain et al. [36]  LQP and Gabor  FERET  Cosine similarity  Lot of discriminative information  Robust to illumination variations  99.2% 75.3% 
LFW  
Gowda et al. [17]  LPQ and LDA  MEPCO  SVM  Computation time  Good accuracy  99.13% 
Z. Cui et al. [67]  BoW  AR  ASM  Occlusions  Robust  99.43% 
ORL  99.50%  
FERET  82.30%  
Khan et al. [83]  PSO and DWT  CK  Euclidienne distance  Noise  Robust to illumination  98.60% 
MMI  95.50%  
JAFFE  98.80%  
Huang et al. [70]  2DDWT  FERET  KNN  Pose  Frontal or nearfrontal facial images  90.63% 97.10% 
LFW  
Perlibakas and Vytautas [69]  PCA and Gabor filter  FERET  Cosine metric  Precision  Pose  87.77% 
Hafez et al. [84]  Gabor filter and LDA  ORL  2DNCC  Pose  Good recognition performance  98.33% 
C. YaleB  99.33%  
Sufyanu et al. [71]  DCT  ORL  NCC  High memory  Controlled and uncontrolled databases  93.40% 
Yale  
Shanbhag et al. [85]  DWT and BPSO  _ _  _ _  Rotation  Significant reduction in the number of features  88.44% 
Ghorbel et al. [61]  Eigenfaces and DoG filter  FERET  Chisquare distance  Processing time  Reduce the representation  84.26% 
Zhang et al. [12]  PCA and FFT  YALE  SVM  Complexity  Discrimination  93.42% 
Zhang et al. [12]  PCA  YALE  SVM  Recognition rate  Reduce the dimensionality  84.21% 
Nonlinear Techniques  
Fan et al. [86]  RKPCA  MNIST ORL  RBF kernel  Complexity  Robust to sparse noises  _ 
Vinay et al. [87]  ORB and KPCA  ORL  FLANN Matching  Processing time  Robust  87.30% 
Vinay et al. [87]  SURF and KPCA  ORL  FLANN Matching  Processing time  Reduce the dimensionality  80.34% 
Vinay et al. [87]  SIFT and KPCA  ORL  FLANN Matching  Low recognition rate  Complexity  69.20% 
Lu et al. [88]  KPCA and GDA  UMIST face  SVM  High error rate  Excellent performance  48% 
Yang et al. [89]  PCA and MSR  HELEN face  ESR  Complexity  Utilizes color, gradient, and regional information  98.00% 
Yang et al. [89]  LDA and MSR  FRGC  ESR  Low performances  Utilizes color, gradient, and regional information  90.75% 
Ouanan et al. [90]  FDDL  AR  CNN  Occlusion  Orientations, expressions  98.00% 
Vankayalapati and Kyamakya [77]  CNN  ORL  _ _  Poses  High recognition rate  95% 
Devi et al. [63]  2FNN  ORL  _ _  Complexity  Low error rate  98.5 
Author/Technique Used  Database  Matching  Limitation  Advantage  Result  

Fathima et al. [91]  GWLDA  AT&T  kNN  High processing time  Illumination invariant and reduce the dimensionality  88% 
FACES94  94.02%  
MITINDIA  88.12%  
Barkan et al., [92]  OCLBP, LDA, and WCCN  LFW  WCCN  _  Reduce the dimensionality  87.85% 
Juefei et al. [93]  ACF and WLBP  LFW  Complexity  Pose conditions  89.69%  
Simonyan et al. [64]  Fisher + SIFT  LFW  Mahalanobis matrix  Single feature type  Robust  87.47% 
Sharma et al. [96]  PCA–ANFIS  ORL  ANFIS  Sensitivityspecificity  96.66%  
ICA–ANFIS  ANFIS  Pose conditions  71.30%  
LDA–ANFIS  ANFIS  68%  
Ojala et al. [97]  DCT–PCA  ORL  Euclidian distance  Complexity  Reduce the dimensionality  92.62% 
UMIST  99.40%  
YALE  95.50%  
Mian et al. [98]  Hotelling transform, SIFT, and ICP  FRGC  ICP  Processing time  Facial expressions  99.74% 
Cho et al. [99]  PCA–LGBPHS  Extended Yale Face  Bhattacharyya distance  Illumination condition  Complexity  95% 
PCA–GABOR Wavelets  
Sing et al. [101]  PCA–FLD  CMU  SVM  Robustness  Pose, illumination, and expression  71.98% 
FERET  94.73%  
AR  68.65%  
Kamencay et al. [102]  SPCAKNN  ESSEX  KNN  Processing time  Expression variation  96.80% 
Sun et al. [103]  CNN–LSTM–ELM  OPPORTUNITY  LSTM/ELM  High processing time  Automatically learn feature representations  90.60% 
Ding et al. [95]  CNNs and SAE  LFW  _ _  Complexity  High recognition rate  99% 
Approaches  Databases Used  Advantages  Disadvantages  Performances  Challenges Handled  

Local  Local Appearance  TDF, CF1999, LFW, FERET, CMUPIE, AR, Yale B, PHPID, YaleB Extended, FRGC2.0, Face94. 

 
KeyPoints 

 
Holistic  Linear  LFW, FERET, MEPCO, AR, ORL, CK, MMI, JAFFE, C. Yale B, Yale, MNIST, ORL, UMIST face, HELEN face, FRGC. 

 
NonLinear 


 
Hybrid  AT&T, FACES94, MITINDIA, LFW, ORL, UMIST, YALE, FRGC, Extended Yale, CMU, FERET, AR, ESSEX. 


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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
Chicago/Turabian StyleKortli, 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