Robust face recognition using 2D and 3D data: Pose and illumination compensation

In this paper, we propose a novel robust face recognition framework named nuclear norm based superposed collaborative representation classifier (NNSCRC) to handle illumination variations, occlusion and undersampled problems in face recognition. Specifically, we develop a superposed linear collaborative representation classifier for robust face recognition by representing the query image in terms of a superposition of the class centroid, the shared intra-class difference, and the low rank error. By representing a face image as the class centroid and the shared intra-class difference, our model can effectively enhance the face recognition performance on undersampled databases. In addition, since the occlusion and illumination variations generally lead to a low-rank error image, we use nuclear norm matrix regression to obtain these low-rank errors, which makes our model able to reconstruct the test image better. Extensive experiments are performed on Extended Yale-B and AR databases, which show the effectiveness of NNSCRC in robust face recognition.

Lack of training samples always affects the performance and robustness of face recognition. Generating virtual samples is one of effective methods to expand the training set. When the virtual samples are able to simulate the variations of facial images including variations of illuminations, facial postures and the facial expressions, the robustness will be enhanced and the accuracy will be improved obviously in the face recognition problem. In this paper, an improved linear representation-based classification combined virtual samples (ILRCVS) is proposed. First, we design a new objective function that simultaneously considers the information of the virtual training samples and the virtual test sample. Second, an alternating minimization algorithm is proposed to solve the optimization problem of the objective function. Finally, a new classification criterion combined with the virtual training and test sample is proposed. Experimental results on the Georgia Tech, FERET and Yale B face databases show that the proposed method is more robust than three state-of-the-art face recognition methods, LRC, SRC and CRC.

This paper describes an approach of human sensing using multi-view image patterns. In particular we will focus on a robust face recognition among various information obtained through human sensing. Since our face recognition method is based on the similarity between the distributions of multi-view face images, how to gather such face images is a key issue to achieve high performance. In order to realize stable gathering face images, we introduce a multi-camera system with six IEEE 13 94 PC cameras installed around the frame of a monitor. We confirmed that the performance of face recognition is improved largely by using the multi-camera system in comparison to a single camera system through experimental results.

Most face recognition methods employ single-bit binary descriptors for face representation. The information from these methods is lost in the process of quantization from real-valued descriptors to binary descriptors, which greatly limits their robustness for face recognition. In this study, we propose a novel weighted feature histogram (WFH) method of multi-scale local patches using multi-bit binary descriptors for face recognition. First, to obtain multi-scale information of the face image, the local patches are extracted using a multi-scale local patch generation (MSLPG) method. Second, with the goal of reducing the quantization information loss of binary descriptors, a novel multi-bit local binary descriptor learning (MBLBDL) method is proposed to extract multi-bit local binary descriptors (MBLBDs). In MBLBDL, a learned mapping matrix and novel multi-bit coding rules are employed to project pixel difference vectors (PDVs) into the MBLBDs in each local patch. Finally, a novel robust weight learning (RWL) method is proposed to learn a set of robust weights for each patch to integrate the MBLBDs into the final face representation. In RWL, a codebook is first constructed by clustering MBLBDs on each local patch to extract a feature histogram. Then, considering that different parts of the face have different degrees of robustness to local changes, a set of weights is learned to concatenate the feature histograms of all local patches into the final representation of a face image. In addition, to further improve the performance for heterogeneous face recognition, a coupled WFH (C-WFH) method is proposed. C-WFH maintains the similarity of the corresponding MBLBDs and feature histograms for a pair of heterogeneous face images by means of a novel coupled feature learning (CFL) method to reduce the modality gap. A series of experiments are conducted on widely used face datasets to analyze the performance of WFH and C-WFH. Extensive experimental results show that WFH and C-WFH outperform state-of-the-art face recognition methods.

This paper presents a novel illumination normalization approach for face recognition under varying lighting conditions. In the proposed approach, a discrete cosine transform (DCT) is employed to compensate for illumination variations in the logarithm domain. Since illumination variations mainly lie in the low-frequency band, an appropriate number of DCT coefficients are truncated to minimize variations under different lighting conditions. Experimental results on the Yale B database and CMU PIE database show that the proposed approach improves the performance significantly for the face images with large illumination variations. Moreover, the advantage of our approach is that it does not require any modeling steps and can be easily implemented in a real-time face recognition system.

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.

In this paper, we investigate how to preprocess bad input face images for robust face recognition, under uneven illumination environments. Proposed filter combination shows nice performance under varying illumination, however, it can not provide the highest performance under normal illumination. We found that the performance of each preprocessing method for compensating illumination is highly affected by working illumination environment. Changing illumination poses a most challenging problem in face recognition. A previous research for illumination compensation has been investigated. This paper proposes a filter block for efficient face recognition. Since no priori knowledge of system working environment can be assumed. The proposed method can decide an optimal configuration of filter block by exploring the filter combination and the associated parameters to unknown illumination conditions. The illumination filter includes Retinex filter, end-in contrast stretching and histogram equalization filter. The proposed method has been tested to robust face recognition in varying illumination conditions (Inha DB, FERET DB). We made in illumination cluster using combined FART. Extensive experiment shows that the proposed system can achieve very encouraging performance in varying illumination environments. We furthermore show how this algorithm can be extended towards face recognition across illumination.KeywordsFace RecognitionFace ImageGabor WaveletFiducial PointFilter CombinationThese 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.

At present, the performance of face recognition system depends much on the variations in illumination. To solve this problem, this paper presents an adaptable face recognition approach that uses filter fusion representation. The key idea is to use context-aware filter fusion to get better image from a bad illumination one. Genetic algorithm is the tool for adaptation for individual context category. These can provide robust face recognition on illumination context-awareness under uneven environments. Gabor wavelet representation can also provide a robust feature for image enhancement. Using these approaches, we have developed a robust face recognition technique that can recognize with a notable success and it has been tested on Inha DB and FERET face images.

Face recognition is an area of computer vision that has attracted a lot of interest from the research community. A growing demand for robust face recognition software in security applications has driven the development of interesting approaches in this field. A large quantity of research in face recognition deals with visible face images. In the visible spectrum the illumination and face expressions changes represent a significant challenge for the recognition system. To avoid these problems, researchers proposed recently the use of 3D and infrared imaging for face recognition. In this work, we introduce a new framework for infrared face recognition using texture descriptors. This framework exploits linear and non linear dimensionality reduction techniques for face learning and recognition in the texture space. Active and passive infrared imaging modalities are used and comparison with visible face recognition is performed. Two multispectral face recognition databases were used in our experiments: Equinox Database (Visible, SWIR, MWIR, LWIR) and Laval University Multispectral Database (Visible, NIR, MWIR, LWIR). The obtained results show high increase in recognition performance when texture descriptors like LBP (Local Binary Pattern) and LTP (Local Ternary Pattern) are used. The best result was obtained in the short wave infrared spectrum (SWIR) using non linear dimensionality reduction techniques.

In recent days, there has been increasing need for recognition of unconstrained face images, such as those collected from the web or captured by mobile devices and video surveillance cameras. In such real world scenarios, human faces could be easily occluded by other objects that make the face recognition task as a complex one. Satisfactory performance has been achieved earlier but often only in the controlled environments. But it is very tedious to obtain holistic face images for unconstrained face recognition. Thus, in order to avoid the degradation of face images and the huge variations due to illumination, pose, occlusion and expression, a new Robust Face Recognition approach is proposed. In this approach, the partial face recognition using Scale Invariant Feature Transform (SIFT) technique is combined with Multi-directional Multi-level Dual Cross Patterns (DCP) technique that makes the recognition task as a robust one when compared to other face recognition approaches. Then, the Robust Point Set Matching (RPSM) is used to match the corresponding stable keypoints from both the gallery image and probe face image. Finally, PNN and K-NN classification are used to classify the face images even with the presence of occlusion, random partial crop, illumination, pose and exaggerated facial expression. The proposed robust face recognition system is evaluated based on the performance parameters such as sensitivity, specificity, accuracy, precision and recall.

In this paper, a noble nonintrusive three-dimensional (3D) face modeling system for random-profile-based 3D face recognition is presented. Although recent two-dimensional (2D) face recognition systems can achieve a reliable recognition rate under certain conditions, their performance is limited by internal and external changes, such as illumination and pose variation. To address these issues, 3D face recognition, which uses 3D face data, has recently received much attention. However, the performance of 3D face recognition highly depends on the precision of acquired 3D face data, while also requiring more computational power and storage capacity than 2D face recognition systems. In this paper, we present a developed nonintrusive 3D face modeling system composed of a stereo vision system and an invisible near-infrared line laser, which can be directly applied to profile-based 3D face recognition. We further propose a novel random-profile-based 3D face recognition method that is memory-efficient and pose-invariant. The experimental results demonstrate that the reconstructed 3D face data consists of more than 50 k 3D point clouds and a reliable recognition rate against pose variation.

In this paper, face recognition algorithms under three low-quality conditions, namely, complex illumination, low resolution and partial occlusion, are summarized, and relatively new face recognition algorithms under low-quality conditions are introduced and explained. Such as the algorithm of face recognition with occlusion based on partition, face recognition algorithm based on Iteratively Reweighted Robust Principal Component and face recognition with occlusion based on MEBML, illumination face recognition based on RPCA and Convolutional Neural Network, face recognition based on Illumination Normalization and Block-based Adaptive Local Ternary Pattern, low-resolution face recognition based on blocking CS-LBP and weighted PCA algorithm, pose Robust low-resolution face recognition via CKEDA and other face recognition algorithm under low-quality conditions. The existing problems and development trend of face recognition under low quality conditions are analyzed.

In this chapter, we describe a new robust face recognition system base on a multi-views face database that derives some 3-D information from a set of face images. We attempt to build an approximately 3-D system for improving the performance of face recognition. Our objective is to provide a basic 3-D system for improving the performance of face recognition. The main goal of this vision system is 1) to minimize the hardware resources, 2) to obtain high success rates of identity verification, and 3) to cope with real-time constraints. Using the multi-views database, we address the problem of face recognition by evaluating the two methods PCA and ICA and comparing their relative performance. We explore the issues of subspace selection, algorithm comparison, and multi-views face recognition performance. In order to make full use of the multi-views property, we also propose a strategy of majority voting among the five views, which can improve the recognition rate. Experimental results show that ICA is a promising method among the many possible face recognition methods, and that the ICA algorithm with majority-voting is currently the best choice for our purposes.

In this paper, a novel approach for illumination normalization under varying lighting conditions is presented. Our approach utilizes the fact that discrete cosine transform (DCT) low-frequency coefficients correspond to illumination variations in a digital image. Under varying illuminations, the images captured may have low contrast; initially we apply histogram equalization on these for contrast stretching. Then the low-frequency DCT coefficients are scaled down to compensate the illumination variations. The value of scaling down factor and the number of low-frequency DCT coefficients, which are to be re-scaled, are obtained experimentally. The classification is done using k-nearest neighbor classification and nearest mean classification on the images obtained by inverse DCT on the processed coefficients. The correlation coefficient and Euclidean distance obtained using principal component analysis are used as distance metrics in classification. We have tested our face recognition method using Yale face database B. The results show that our method performs without any error (100% face recognition performance) even on the most extreme illumination variations. There are different schemes in the literature for illumination normalization under varying lighting conditions, but no one is claimed to give 100% recognition rate under all illumination variations for this database. The proposed technique is computationally efficient and can easily be implemented for real time face recognition system.

This study studies different lighting processing and feature extraction methods for efficient face recognition. The purpose is to find some robust face recognition methods by combining different feature extraction methods with illumination compensation. In this study, some typical illumination preprocessing approaches are reviewed including wavelet transformation, self‐quotient image, Retinex, smoothing, discrete cosine transform normalisation in logarithm domain, homomorphic filter and local contrast enhancement. As the main contribution, this study proposes two efficient feature extraction methods for face recognition. One is an adaptive feature extraction (AFE) based on curvelet transform. The other is a feature extraction technique named two‐dimensional principal component analysis (2DPCA) non‐parametric analysis of 2D subspace (2DPCA + 2DNSA). Two groups of experiments are designed to verify the proposed methods. The first group of experimental results show that the proposed AFE methods have better performance than conventional methods. In the second group of experiments, each feature extraction method is combined with nine different lighting processing methods. The results show that the proposed 2DPCA + 2DNSA method is more robust to lighting processing than other methods. Experimental results also show that lighting processing contribution to face recognition are quite different for different face databases.