Dual sparse learning via data augmentation for robust facial image classification

Facial recognition is a challenging area of research due to difficulties with robust face recognition (FR) under occlusion and sparse representation-based classification (SRC) only focusing on face global features. To solve these issues, we proposed an occluded FR method based on dictionary learning for sparse representation and sub-classifiers fusion (LSSRC), which efficiently combines local and overall characteristics of face images. First, we partitioned continuous but non-lapped blocks of the face by multi-resolution blocking. Then, for each block, SRC was used for feature extraction and face classification. We established a sub-block dictionary and conducted K-SVD dictionary learning, established sub-classifiers and determined weight. Finally, we conducted sub-classifier fusion recognition using voting rules with weight. Results using the AR and YaleA database showed that our algorithm achieved superior recognition performance to the existing sparse representation classification occluded FR method.

Joint representation and pattern learning for robust face recognition

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.

This paper presents a block-based face-recognition algorithm based on a sparse linear-regression subspace model via locally adaptive dictionary constructed from past observable data (training samples). The local features of the algorithm provide an immediate benefit — the increase in robustness level to various registration errors. Our proposed approach is inspired by the way human beings often compare faces when presented with a tough decision: we analyze a series of local discriminative features (do the eyes match? how about the nose? what about the chin?…) and then make the final classification decision based on the fusion of local recognition results. In other words, our algorithm attempts to represent a block in an incoming test image as a linear combination of only a few atoms in a dictionary consisting of neighboring blocks in the same region across all training samples. The results of a series of these sparse local representations are used directly for recognition via either maximum likelihood fusion or a simple democratic majority voting scheme. Simulation results on standard face databases demonstrate the effectiveness of the proposed algorithm in the presence of multiple mis-registration errors such as translation, rotation, and scaling.

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.

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.

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.

Plurality voting is widely employed as combination strategies in pattern recognition. As a technology proposed recently, sparse representation based classification codes the query image as a sparse linear combination of entire training images and classifies the query sample class by class exploiting the class representation error. In this paper, an improvement face recognition approach using sparse representation and plurality voting based on the binary bit-plane images is proposed. After being equalized, gray images are decomposed into eight bit-plane images, sparse representation based classification is exploited respectively on the five bit-plane images that have more discrimination information. Finally, the true identity of query image is voted by these five identities obtained. Experiment results shown that this proposed approach is preferable both in recognition accuracy and in recognition speed.

Although face recognition applications are growing, robust face recognition is still a challenging task due e.g. to variations in face poses, facial expressions, or lighting conditions. In this paper, we propose a new method which allows both automatic face detection and recognition and incorporates an interactive selection of facial features in conjunction with our new pose-correction algorithm. Our resulting system we called iFR successfully recognizes faces across pose, while being computationally efficient and outperforming standard approaches, as demonstrated in tests carried out on publicly available standard datasets.

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 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.

Face recognition is a biological technology based on identity recognition of human face feature information. In this study, Web of Science Core Collection database is used as data source. Using Excel, Bibexcel and VOSviewer software for data mining and quantitative analysis of the 1980-2018 year research papers on face recognition, we explores the trend of change, the distribution of research forces, key areas, research hot spots and international cooperation. The results show that the number of documents in face recognition has been growing, and there are many subjects involved in the research of face recognition technology, mainly Computer Science, Enginerring and}{Psychology. There are many research papers on face recognition in China and the United States; China is most prominent in international cooperation and has strong cooperative relations with all major countries; the Chinese Academy of Sciences is very active in the research of face recognition, and its papers are far ahead of other institutions. The main focuses of the study are feature extraction, face detection, sparse representation, face perception, etc. The main hot spots of research are sparse representation, dictionary learning, deep learning, semisupervised learning, metric learning, sparse coding, etc.

We describe a new method for recognizing humans by their face, which is robust to the variations of facial imaging conditions, with high accuracy. The human face recognition system consists of three components: i) a new face descriptor based on edge component histogram and its variance between pixels, ii) Gab or-edge components histogram for facial image representation, combining the Gab or wavelet and the proposed edge components histogram, iii) a sparse representation classifier for the face recognition. The effective and robust face recognition with high accuracy is achieved by the Gab or-edge components histogram and the sparse representation classifier. In experiments, higher face recognition performances, which are 99.45% on ETRI database and 99.41% on XM2VTS database, have been achieved.

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

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.