Face recognition committee machine

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%).

Chapter 10 - Local plastic surgery-based face recognition using convolutional neural networks

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.

In this paper, a robust face recognition algorithm is proposed, which is based on the elastic graph matching (EGM) and discriminative feature analysis algorithm. We introduce a cost function for the EGM taking account of variations in face pose and facial expressions, and propose its optimization procedure. Our proposed cost function uses a set of Gabor-wavelet-based features, called robust jet, which are robust against the variations. The robust jet is defined in terms of discrete Fourier transform coefficients of Gabor coefficients. To cope with the difference between face poses of test face and reference faces, 2 x 2 warping matrix is incorporated in the proposed cost function. For the discriminative feature analysis, linear projection discriminant analysis and kernel-based projection discriminant analysis are introduced. These methods are motivated to solve the small-size problem of training samples. The basic idea of PDA is that a class is represented by a subspace spanned by some training samples of the class instead of using sample mean vector, that the distance from a pattern to a class is defined by using the error vector between the pattern and its projection to the subspace representing the class, and that an optimum feature selection rule is developed using the distance concept in a similar way as in the conventional linear discriminant analysis. In order to evaluate the performance of our face recognition algorithm, we carried out some experiments using the well-known FERET face database, and compared the performance with recently developed approaches. We observed that our algorithm outperformed the compared approaches.

Face recognition technology has evolved as a popular identification technique to perform verification of human identity. By using the feature extraction methods and dimensionality reduction techniques in the pattern recognition applications, a number of facial recognition systems has been produced with distinct measure of success. Various face recognition algorithms and their extensions, have been proposed in the past three decades. However, face recognition faces challenging problems in real life applications because of the variation in the illumination of the face images. In the recent years, the research is focused towards Illumination-invariant face recognition system and many approaches have been proposed. But, there are several issues in face recognition across illumination variation which still remains unsolved. This paper provides a research on an efficient illumination-invariant face recognition system using Discrete Cosine Transform (DCT) and Principal Component Analysis (PCA). For processing the illumination invariant image, low frequency components of DCT are used to normalize the illuminated image, odd and even components of DCT is used for compensation in illumination variation and PCA is used for recognition of face images. The existing approaches in illumination Invariant face recognition are comprehensively reviewed and discussed. The proposed approach is validated with Yale Face Database B. Experimental results demonstrate the effectiveness of this approach in the performance of face recognition.

This paper describes in detail about 2D and 3D face and video face databases developed in the year from 1992 to 2009. The still face database discussed here includes: Our Laboratory Database of Faces (ORL), Yale face database, Yale face databaseB(B stands for Big), Yale face database extendedB, FERET(Face Recognition Technology) face database and CASPEAL is a large scale Chinese face database with varying PEAL(Pose Expression Accessory, and Lighting). The 3D face databases like MIT-CBCL (Centre for Biological and Computational Learning) face recognition database, Georgia Tech face database, Cohn-Kanade Action Unit Face database and 3D BFM (Basel Face Model) are also discussed in this paper. In CK AU, CK+AU(Cohn-Kanade Action Unit) coded facial expression database, a 3D model is developed from a 2D model which is obtained from the captured video of different subjects. The other Face databases and development of new Face databases are discussed at the end of this paper. The application of proposed concept is to develop a new small size face database which includes all the important criteria included in the previous face and video face databases.

In this paper, an algorithm is proposed to recognize faces with illumination variation. First the reflectance at every point is estimated by symmetry on face from two images in different known illumination. Then face images in different illumination are generated by the reflectance. Finally, Face recognition is implemented by comparing the similarity between test image and generated images. Experimental results from Yale Face Database B confirm the efficiency of our algorithm.

Facial images are of critical importance in many real-world applications from gaming to surveillance. The current literature on facial image analysis, from face detection to face and facial expression recognition, are mainly performed in either RGB, Depth (D), or both of these modalities. But, such analyzes have rarely included Thermal (T) modality. This paper paves the way for performing such facial analyzes using synchronized RGB-D-T facial images by introducing a database of 51 persons including facial images of different rotations, illuminations, and expressions. Furthermore, a face recognition algorithm has been developed to use these images. The experimental results show that face recognition using such three modalities provides better results compared to face recognition in any of such modalities in most of the cases.

A human face provides a variety of different communicative functions such as identification, the perception of emotional expression, and lip-reading. Many applications in robotics require recognizing a human face. A face recognition system should be able to deal with various changes in face images, such as pose, illumination, and expression, among which pose variation is the most difficult one to deal with. For this reason, face registration is the key of face recognition. If we can register face images into frontal views, the recognition task would be much easier. To align a face image into a canonical frontal view, we need to know the pose information of a human head. A human head can be approximately modeled as a 3D texture mapped ellipsoid. Then, any face image can be considered as a 2D image projection of a 3D ellipsoid at a certain pose. In this paper, both training and test face images are back projected to the surface of a 3D ellipsoid according to their estimated poses and registered into canonical frontal view images. Then, simple and efficient frontal face recognition can be carried out in the texture map domain instead of the original input image domain. To evaluate the feasibility of the proposed approach, several recognition experiments are conducted using subspace-based face recognition methods such as PCA, PCA+LAD, and DCV. By conducting experiments on our laboratory database and the Yale Face Database B, we show that the proposed algorithm provides good performance even when large out-of-plane rotations occur.

In this paper, a novel method for independent component analysis (ICA) with 2-D Principle Component Analysis (2DPCA) in face recognition is presented, called 2DPCA-ICA. In this method, 2DPCA is used for dimension reduction, and ICA for recognition. As opposed to the method for ICA based on PCA in face recognition (PCA-ICA), 2DPCA is based on 2-D face image matrix, an image covariance matrix is constructed directly using 2-D image matrix, and its eigenvectors corresponding to the several larger eigenvalues are derived for whitened image matrix. It overcomes the shortcomings of PCA-ICA. To test 2DPCA-ICA and evaluate its performance, experiments are performed on Yale and ORL (Olivetti Research Laboratory) face database. Correct recognition rate of 2DPCA-ICA across all trials is higher than that of PCA-ICA and 2DPCA. Experimental results also show that features of face image extracted are more efficient by way of 2DPCA-ICA. Therefore, 2DPCA-ICA is more valid in face recognition.

In this paper, a novel approach is presented for adaptive illumination normalization for face recognition under varying illuminations due to change in angle of light projection. Illumination normalization is performed over some of the low frequency discrete Cosine transform (DCT) coefficients which are computed adaptively based upon the significance of alteration of these coefficients. These are, then, modified using a non-linear modifier. The significance of the proposed approach is that the approach is adaptive in normalizing the illumination from the face images as the number of low frequency DCT coefficients that need to be modified using non-linear modifier are selected on the basis of variations of illumination present in the image. This variation in the illumination is also used in developing the non-linear modifier. The proposed approach is important in such a way that the level of illumination variations decides the computations needed i.e. lesser in comparison to the other existing state-of-art approaches as large number of frequency coefficients are not altered. The proposed approach is tested over various face databases: YALE B, Extended YALE B, CMU PIE, AR and YALE face database. The experimental results clearly reveal that the performance of the proposed approach is significantly better than the existing approaches of illumination normalization for face recognition. With the proposed approach, 100% accuracy is attained on all the subsets of YALE B, CMU PIE and on Subset 3 of Extended YALE B database. Promising results are achieved over remaining face databases as well.

Biometric features such as fingerprints, iris patterns, and face features help to identify people and restrict access to secure areas by performing advanced pattern analysis and matching. Face recognition is one of the most promising biometric methodologies for human identification in a non-cooperative security environment. However, the recognition results obtained by face recognition systems are a affected by several variations that may happen to the patterns in an unrestricted environment. As a result, several algorithms have been developed for extracting different facial features for face recognition. Due to the various possible challenges of data captured at different lighting conditions, viewing angles, facial expressions, and partial occlusions in natural environmental conditions, automatic facial recognition still remains as a difficult issue that needs to be resolved. In this paper, we propose a novel approach to tackling some of these issues by analyzing the local textural descriptions for facial feature representation. The textural information is extracted by an enhanced local binary pattern (ELBP) description of all the local regions of the face. The relationship of each pixel with respect to its neighborhood is extracted and employed to calculate the new representation. ELBP reconstructs a much better textural feature extraction vector from an original gray level image in different lighting conditions. The dimensionality of the texture image is reduced by principal component analysis performed on each local face region. Each low dimensional vector representing a local region is now weighted based on the significance of the sub-region. The weight of each sub-region is determined by employing the local variance estimate of the respective region, which represents the significance of the region. The final facial textural feature vector is obtained by concatenating the reduced dimensional weight sets of all the modules (sub-regions) of the face image. Experiments conducted on various popular face databases show promising performance of the proposed algorithm in varying lighting, expression, and partial occlusion conditions. Four databases were used for testing the performance of the proposed system: Yale Face database, Extended Yale Face database B, Japanese Female Facial Expression database, and CMU AMP Facial Expression database. The experimental results in all four databases show the effectiveness of the proposed system. Also, the computation cost is lower because of the simplified calculation steps. Research work is progressing to investigate the effectiveness of the proposed face recognition method on pose-varying conditions as well. It is envisaged that a multilane approach of trained frameworks at different pose bins and an appropriate voting strategy would lead to a good recognition rate in such situation.

In recent years, technologies related to face recognition have achieved rapid development. Face recognition Algorithms using Convolutional Neural Network (CNN) in the field of deep learning have been improved compared with traditional ones in terms of recognition accuracy, anti-interference ability and the speediness of identification. The mature face recognition technology now plays a decisive role in many areas, and also one of the key technologies in the facilitation of unmanned supermarkets. However, most algorithms nowadays just use the face database in unrestricted scenes to train, verify and evaluate. Their performance in practical applications is not satisfactory enough. In this paper, we eager to find a face recognition algorithm and system that are more suitable for unmanned supermarket scenes. After in-depth analysis of the scene, we find out that motion blur and large-scale recognition are two important reasons why current algorithms do not perform well. Aim to solve these problems, we summarize and propose new algorithms based on the existing ones. The experimental results show that the algorithm has effective face recognition ability in unmanned supermarket.

In this paper, we describe a new approach in face recognition by which the recognition accuracy can be increased substantially. In our approach a detail correlation study has been made on standard database such as Yale face database. Based on correlation coefficient, face images of individuals are duplicated in train set or test set or in both category. Then face databases are tested with standard face recognition algorithms such as Eigen face, Fischer face, KPCA, ICA and 2DPCA. In all these methods, arrangement of faces based on our approach gives better result. The software used to test all these algorithms is open source Scilab.

Face recognition is a common means of identity authentication. Mobile learning platform login technology has developed from user name and password to face recognition. In order to improve effectively the rate of face recognition, this paper proposes a kind of face recognition algorithm based on self-adaptive blocking local binary pattern (LBP) and dual channel convolutional neural network (CNN) with different convolution kernels. Firstly, the Gamma correction, the Mallet wavelet filtering and normalization are used to preprocess the face image. The face image is decomposed and reconstructed by 2-layer Mallet wavelet to filter out the interference signal effectively. Although the general LBP operator extracts the overall texture and contour features of the face image, the distribution of the bright spot, dark spot and other micro details cannot be fully characterized. In order to solve this problem, integral projection is introduced to project the image horizontally and vertically. The extreme points of the projection represent the texture mutation points of the face image in the horizontal and vertical directions. These extreme points are used as the boundary of the image blocking, and the LBP value of the face image is extracted by the self-adaptive blocking strategy. Combining the features of k-nearest neighbor classifier and softmax, a k-softmax classification method is proposed to classify and recognize the face image labels. After two channel network structure training, this method is tested on Yale, ORL, extended Yale B and self-built face databases by five experiments, comparing with other face recognition algorithms. The results show that the proposed method based on SAB-LBP and dual channel CNN has high recognition rate and computational efficiency.