Study of Face Recognition Techniques: A Survey

One of the main issues of face recognition is to extract features from face images, which include both local and global features. We present a novel method to perform feature fusion at the feature level. First, global features are extracted by principal component analysis (PCA), while local features are obtained by active appearance model (AAM) and Gabor wavelet transform (GWT). Second, two types of features are fused by weighted concatenation. Finally, Euclidean and feature distances of fused features are applied to carry out a nearest neighbor classifier. The method is evaluated by the recognition rates and computation cost over two face image databases [AR (created by A. Martinez and R. Benavente) and SJTU-IPPR (Shanghai JiaoTong University-Institute of Image Processing and Pattern Recognition)]. Compared with PCA and elastic bunch graph matching (EBGM), the presented method is more effective. Though the recognition rate of the presented method is not as good as nonlinear feature combination (NFC), low computation cost is its superiority. In addition, experimental results show that the novel method is robust to variations over time, expression, illumination, and pose to a certain extent.

Face Recognition is an efficient technique and one of the most liked biometric software application for the identification and verification of specific individual in a digital image by analysing and comparing patterns. This paper presents a survey on well-known techniques of face recognition. The primary goal of this review is to observe the performance of different face recognition algorithms such as SVM (Support Vector Machine), CNN (Convolutional Neural Network), Eigenface based algorithm, Gabor Wavelet, PCA (Principle Component Analysis) and HMM (Hidden Markov Model). It presents comparative analysis about the efficiency of each algorithm. This paper also figure out about various face recognition applications used in real world and face recognition challenges like Illumination Variation, Pose Variation, Occlusion, Expressions Variation, Low Resolution and Ageing in brief. Another interesting component covered in this paper is review of datasets available for face recognition. So, must needed survey of many recently introduced face recognition aspects and algorithms are presented.

Face recognition at a distance is one grand challenge for security surveillance. In this paper, the face images at different distances are simulated by varying image scales (resolutions). The performances of three face recognition algorithms (matchers) are tested with variant image scales (simulating different distances) and with two spectral images (modalities). The three selected matchers are face pattern byte, elastic bunch graph matching, and linear discriminant analysis; while the two modalities are visible and thermal images. The performance of a face recognition system can be measured by accuracy (AC) rate and false accept rate (FAR). To enhance the performance of face recognition especially at a distance, score fusion techniques are applied, which combine several scores from multiple matchers and multiple modalities. Our experiments are conducted with the ASUMS face dataset consisting of two spectral images (visible and thermal) from 135 subjects. The experimental results show that the face recognition with small image scales (simulating long distances) have low performance (e.g., AC=91.36%, FAR=8.64% for 20×20-pixel images); and score fusion can greatly improve accuracy (99.34%) meanwhile reduce FAR (0.31%).

Face detection is the first step in the face recognition system. The Neural network as well as Gabor wavelet based face detection is proposed in this paper. The proposed system applies neural network algorithm to make the slanting face upright and Gabor wavelet is used to extract the invariable features from the selected window. The principal component analysis is applied to identify the person from the data base. The Proposed system gives very high detection efficiency with low number of false alarms. We have shown the results on different test sets with different degrees of variability of the face patterns. In this paper, we present a new approach for determining the orientation of face in the selected window and then use this information to rotate the face upright

This paper presents a novel Gabor-based kernel Principal Component Analysis (PCA) method by integrating the Gabor wavelet representation of face images and the kernel PCA method for face recognition. Gabor wavelets first derive desirable facial features characterized by spatial frequency, spatial locality, and orientation selectivity to cope with the variations due to illumination and facial expression changes. The kernel PCA method is then extended to include fractional power polynomial models for enhanced face recognition performance. A fractional power polynomial, however, does not necessarily define a kernel function, as it might not define a positive semidefinite Gram matrix. Note that the sigmoid kernels, one of the three classes of widely used kernel functions (polynomial kernels, Gaussian kernels, and sigmoid kernels), do not actually define a positive semidefinite Gram matrix either. Nevertheless, the sigmoid kernels have been successfully used in practice, such as in building support vector machines. In order to derive real kernel PCA features, we apply only those kernel PCA eigenvectors that are associated with positive eigenvalues. The feasibility of the Gabor-based kernel PCA method with fractional power polynomial models has been successfully tested on both frontal and pose-angled face recognition, using two data sets from the FERET database and the CMU PIE database, respectively. The FERET data set contains 600 frontal face images of 200 subjects, while the PIE data set consists of 680 images across five poses (left and right profiles, left and right half profiles, and frontal view) with two different facial expressions (neutral and smiling) of 68 subjects. The effectiveness of the Gabor-based kernel PCA method with fractional power polynomial models is shown in terms of both absolute performance indices and comparative performance against the PCA method, the kernel PCA method with polynomial kernels, the kernel PCA method with fractional power polynomial models, the Gabor wavelet-based PCA method, and the Gabor wavelet-based kernel PCA method with polynomial kernels.

The current national standards of face recognition do not put the placement of light for a clear description, only said that the light should be uniform when face recognition, no overexposure and underexposure. In this paper, we present a novel database and use different face recognition algorithms to determine the placement of light during face recognition. For this study, we have chosen ten widely-used face recognition algorithms: principal component analysis(PCA), linear discriminant analysis(LDA), independent component analysis(ICA), nonnegative matrix factorization (NMF), histogram of oriented gradient(HOG), scale-invariant feature transform(SIFT), face recognition, CosFace, ArcFace, and Combined Margin. We use a novel database that contains face images with a wide range of illumination angles to analyze the relationship between these algorithms and illumination angles. The results of this experiment indicate that the horizontal illumination angle of the light should be controlled in the range from -10° to 10° and the vertical illumination angle should be controlled in the range from -10° to 10°.

The literature on independent component analysis (ICA)-based face recognition generally evaluates its performance using standard principal component analysis (PCA) within two architectures, ICA Architecture I and ICA Architecture II. In this correspondence, we analyze these two ICA architectures and find that ICA Architecture I involves a vertically centered PCA process (PCA I), while ICA Architecture II involves a whitened horizontally centered PCA process (PCA II). Thus, it makes sense to use these two PCA versions as baselines to reevaluate the performance of ICA-based face-recognition systems. Experiments on the FERET, AR, and AT&T face-image databases showed no significant differences between ICA Architecture I (II) and PCA I (II), although ICA Architecture I (or II) may, in some cases, significantly outperform standard PCA. It can be concluded that the performance of ICA strongly depends on the PCA process that it involves. Pure ICA projection has only a trivial effect on performance in face recognition.

Not alike to principal component analysis (PCA) based monitoring statistics (T2 and SPE), the control limits for independent component analysis (ICA) monitoring statistics (I2 , Ie 2 and SPE) cannot be determined directly from a particular approximation distribution due to latent variables do not follow Gaussian distribution. Lee et al. (2004) proposed to use kernel density estimation (KDE) to obtain the control limits. However, the KDE method is very sensitive to the choice of smoothing parameter. Therefore, this study utilizes the support vector machine (SVM) for process fault detection by taking information of ICA extracted statistics as inputs of SVM. The proposed method (named, ICA-SVM) will be implemented in the Tennessee Eastman Process. In which, several multivariate monitoring schemes such as PCA, ICA, modified ICA and ICA- PCA will be also compared to demonstrate the efficiency of proposed method.

This paper pertains to the recognition of human face within the color images. The proposed scheme used the clue of skin color to detect faces within the color images, and Mallat wavelet transform was applied to get the LL2 frequency subband image of the original faces, then, features were extracted from the raw data by using principal component analysis (PCA). These discriminative and low dimensional features achieved were fed to a support vector machines (SVMs) with polynomial kernel for the training and fuzzy theory was applied on multi-class classification to deal with the unclassifiable cases. Experiments were conducted using the indoors photographs and the results demonstrated that the proposed methodology was efficient for the frontal faces detection and recognition.

A novel Gabor-Kernel face recognition method is proposed in this paper. This involves convolving a face image with a series of Gabor wavelets at different scales, locations, and orientations and extracting features from resulting Gabor filtered images. kernel discriminant analysis (KDDA) is then applied to the feature vectors for dimension reduction as well as class separability enhancement. A database of 600 frontal-view face images from the FERET face database is used to test the method. Experimental results demonstrate the advantage of KDDA over other Kernel methods such as kernel principal component analysis (KPCA) and general discriminant analysis (GDA). Significant improvements are also observed when features are extracted from Gabor filtered images instead of the original images. A 94% accuracy has been observed for the novel Gabor + KDDA method on the FERET database using a simple classifier, which could be further improved by employing a more complex classifier and distance measurer.

In this paper, we report a comprehensive study of two well evolved and developed learning algorithms for effective Face Recognition (FR); viz. the Hidden Markov Model (HMM) and Support Vector Machines (SVM). It is evident that, the accuracy of recognition and efficiency in terms of time and speed of a FR system are directly proportional to the competency of the underlying learning algorithms. Here, we propose to compare the two acclaimed method stated above. A component-based approach is adapted to train face images for recognition. Each face image is divided into sub-states for both HMM and SVM algorithm. In attempt to achieve better rates of recognition, all face images are pre-processed and resizing which helps in reducing the overall complexity of the FR system. We run this proposed system against benchmarks accredited by previous researches.

Face recognition of persons has received so much attention in the recent years due to its many applications in different fields such as security applications, video surveillance, biometric systems, identifying the criminals etc. This paper develops a system that can recognize the human face in the input image after it has been detected. The system is trained with set of faces and non faces, and when the input picture is given, the face is detected using Viola Jones Algorithm. In face recognition, the features are extracted from the training dataset using Principal Component Analysis (PCA) and then the system is trained to recognize the face using Support Vector Machine (SVM) classification. When the input image is given for face recognition, features are extracted from the input picture using PCA and multiclass classification is done by SVM.

Support Vector Machines (SVM) and Independent Component Analysis (ICA) are two powerful and relatively recent techniques. SVMs are classifiers which have demonstrated high generalization capabilities in many different tasks, including the object recognition problem. ICA is a feature extraction technique which can be considered a generalization of Principal Component Analysis (PCA). ICA has been mainly used on the problem of blind signal separation. In this paper we combine these two techniques for the face recognition problem. Experiments were made on two different face databases, achieving very high recognition rates. As the results using the combination PCA/SVM were not very far from those obtained with ICA/SVM, our experiments suggest that SVMs are relatively insensitive to the representation space. Thus as the training time for ICA is much larger than that of PCA, this result indicates that the best practical combination is PCA with SVM.KeywordsSupport Vector MachineFace RecognitionFace ImageIndependent Component AnalysisFace DatabaseThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Face recognition is a significant biometric credential in the field of security authentication. It additionally assumes a noteworthy job in image processing and it is applicable in various systems like verifying the identity of the person and in security purpose. Recognizing the face with varying background, poses and illumination are the complexity involved in this face recognition. Many algorithms exist for face recognition, of which, Discrete Wavelet Transform (DWT) with Principal Component Analysis (PCA) works better for recognition of faces. An algorithm using 3 Level-DWT and modified PCA is proposed for feature extraction. The PCA and reconstruction of images using Inverse PCA, help not only for dimensionality reduction, but also to find the least principal components (PC) of an image from which the significant features of a face image can be extracted. The significant features thus extracted are used for classifying genetic and non-genetic faces. Using extracted features from 3 level DWT and PCA, Support vector machine (SVM) is utilized to classify the faces genetically. The proposed extracted features does not intend to certain features like ears, nose and eyes of the face, but corresponds to identify the faces which are genetically similar. Based on the statistical measure analysis, the proposed algorithm 3 Level dwt with modified PCA works well in extracting the features for identifying the faces which are genetically closer. This face recognition application system can be effectively used to treat a patient in other location with complete security. There is no chance for data stealing, since the concerned doctors and patient only will take part in the system. The identification of genetic faces will turn out to be an achievement in the field of health care monitoring systems.

A face recognition system usually consists of one recognition algorithm by processing single spectral images. For example, face pattern byte (FPB) algorithm was initially created using thermal (LWIR) images, while Elastic Bunch Graphic Matching (EBGM) algorithm was originated with visible (RGB) images. When there are two or more recognition algorithms and/or spectral images available, system performance can be enhanced using information fusion. In this paper, a score fusion with multispectral images is proposed to improve system performance, which is termed as an integrated multispectral face recognition system. Score fusion actually combines several scores from multiple matchers (algorithms) and/or multiple modalities (multispectra). The system performance is measured by the recognition accuracy (AC; the higher the better) and false accept rate (FAR; the lower the better). Specifically, a fusion method will combine the face scores from three matchers (Circular Gaussian Filter, FPB, EBGM) and from two-spectral bands (visible and thermal). We present and compare the system performance using seven fusion methods: linear discriminant analysis (LDA), k-nearest neighbor (KNN), support vector machine (SVM), binomial logistic regression (BLR), Gaussian mixture model (GMM), artificial neural network (ANN), and hidden Markov model (HMM). Our experiments are conducted with the Alcon State University multispectral face dataset that currently consists of two spectral images from 105 subjects. The experimental results show that the KNN score fusion produces the best performance (AC = 98.98%; FAR = 0.35%); and the SVM yields the second best. Compared with the performance of the single best matcher (AC = 91.67%, FAR = 8.33%), the integrated system with score fusion highly improves the accuracy, meanwhile dramatically reduces the FAR.