Weighted Feature Histogram of Multi-Scale Local Patch Using Multi-Bit Binary Descriptor for Face Recognition.

In this work, a simple angular reconstruction based binary descriptor (ARBD) strategy is proposed for face recognition. Compared with previous learning-based hashing methods such as compact binary face descriptor (CBFD) and sparse projection matrix binary descriptor (SPMBD) which measure data similarity with the Euclidean distance, our ARBD focuses on using the cosine similarity to generate binary codes. Specifically, the angular reconstruction term is added to the objective function to minimize the reconstruction error. Furthermore, an efficient algorithm based on the augmented Lagrange method (ALM) is designed to explicitly address the discrete constraint in the Hamming space. We also perform pooling on the learned binary codes with the unsupervised clustering manner to improve their discriminative ability. The results on two face datasets (i.e., CAS-PEAL-R1 and PaSC) demonstrate the superior performance of our ARBD over other existing face recognition methods.

Face recognition has always been a challenging task in real-life surveillance videos, with partial occlusion being one of the key factors affecting the robustness of face recognition systems. Previous researches had approached the problem of face recognition with partial occlusions by dividing a face image into local patches, and training an independent classifier for each local patch. The final recognition result was then decided by integrating the results of all local patch classifiers. Such a local approach, however, ignored all the crucial distinguishing information presented in the global holistic faces. Instead of using only local patch classifiers, this paper presents a novel multi-level supporting scheme which incorporates patch classifiers at multiple levels, including both the global holistic face and local face patches at different levels. This supporting scheme employs a novel criteria-based class candidates selection process. This selection process preserves more class candidates for consideration as the final recognition results when there are conflicts between patch classifiers, while enables a fast decision making when most of the classifiers conclude to the same set of class candidates. All the patch classifiers will contribute their supports to each selected class candidate. The support of each classifier is defined as a simple distance-based likelihood ratio, which effectively enhances the effect of a “more-confident” classifier. The proposed supporting scheme is evaluated using the AR face database which contains faces with different facial expressions and face occlusions in real scenarios. Experimental results show that the proposed supporting scheme gives a high recognition rate, and outperforms other existing methods.

Human face recognition is one of the most important research areas in biometrics. However, the robust face recognition under a drastic change of the facial pose, expression, and illumination is a big challenging problem for its practical application. Such variations make face recognition more difficult. In this paper, we propose a novel face recognition method, called Attentional Feature-pair Relation Network (AFRN), which represents the face by the relevant pairs of local appearance block features with their attention scores. The AFRN represents the face by all possible pairs of the 9x9 local appearance block features, the importance of each pair is considered by the attention map that is obtained from the low-rank bilinear pooling, and each pair is weighted by its corresponding attention score. To increase the accuracy, we select top-K pairs of local appearance block features as relevant facial information and drop the remaining irrelevant. The weighted top-K pairs are propagated to extract the joint feature-pair relation by using bilinear attention network. In experiments, we show the effectiveness of the proposed AFRN and achieve the outstanding performance in the 1:1 face verification and 1:N face identification tasks compared to existing state-of-the-art methods on the challenging LFW, YTF, CALFW, CPLFW, CFP, AgeDB, IJB-A, IJB-B, and IJB-C datasets.

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.

Facial albedo images are believed to be invariant to external factors of pose, illumination and expression that can greatly affect the appearance of face images and thus face recognition accuracy as well. Unlike most existing face recognition methods that address the impact of one or two of these external factors, we propose an end-to-end network, which consists of De-Light Network (DL-Net) and Normalization Network (N-Net), to generate normalized albedo images with neutral expression and frontal pose for input face images. DL-Net aims to eliminate the effects of illumination and reconstruct a posed albedo image that has the same pose and expression as the input image. N-Net attempts to generate a pose and expression normalized albedo image and extract identity features under the supervision of the normalized albedo images. Our experiments on the Multi-PIE database show that the extracted identity features can effectively assist conventional face recognition methods to improve face recognition accuracy under varying poses, illuminations and expressions.

Adaptive illumination-invariant face recognition via local nonlinear multi-layer contrast feature

In the literature of psychophysics and neurophysiology, many studies have shown that both global and local features are crucial for face representation and recognition. This paper proposes a novel face recognition method which combines both global and local discriminative features. In this method, global features are extracted from whole face images by Fourier transform and local features are extracted from some spatially partitioned image patches by Gabor wavelet transform. After this, multiple classifiers are obtained by applying Fisher Discriminant Analysis on global Fourier features and local patches of Gabor features. All these classifiers are combined to form a hierarchical ensemble by sum rule. We evaluated the proposed method using Face Recognition Grand Challenge (FRGC) experimental protocols and database known as the largest data sets available. Experimental results on FRGC version 2.0 data set have shown that the proposed method achieves a verification rate of 86%, while the best reported was 76%.

Depth information has been proven useful for face recognition. However, existing depth-image-based face recognition methods still suffer from noisy depth values and varying poses and expressions. In this paper, we propose a novel method for normalizing facial depth images to frontal pose and neutral expression and extracting robust features from the normalized depth images. The method is implemented via two deep convolutional neural networks (DCNN), normalization network (\(Net_{N}\)) and feature extraction network (\(Net_{F}\)). Given a facial depth image, \(Net_{N}\) first converts it to an HHA image, from which the 3D face is reconstructed via a DCNN. \(Net_{N}\) then generates a pose-and-expression normalized (PEN) depth image from the reconstructed 3D face. The PEN depth image is finally passed to \(Net_{F}\), which extracts a robust feature representation via another DCNN for face recognition. Our preliminary evaluation results demonstrate the superiority of the proposed method in recognizing faces of arbitrary poses and expressions with depth images.

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.

A new method of face recognition based on gradient direction histogram (HOG) features extraction and fast principal component analysis (PCA) algorithm is proposed to solve the problem of low accuracy of face recognition under non-restrictive conditions. In this method, the Haar feature classifier is used to extract and extract the original data, and then the HOG features are extracted from the image data and the PCA dimension reduction is processed, and the Support Vector Machines (SVM) algorithm is used to recognize the face. The experimental results of the classification recognition on the LFW face database verify the effectiveness of the method.

The automatic recognition of human faces presents a significant challenge to the pattern recognition research community. Typically, human faces are very similar in structure with minor differences from person to person. They are actually within one class of "human face". Furthermore, lighting conditions change, while facial expressions and pose variations further complicate the face recognition task as one of the difficult problems in pattern analysis. This paper proposes a novel concept: namely, that faces can be recognized using a line edge map (LEM). The LEM, a compact face feature, is generated for face coding and recognition. A thorough investigation of the proposed concept is conducted which covers all aspects of human face recognition, i.e. face recognition under (1) controlled/ideal conditions and size variations, (2) varying lighting conditions, (3) varying facial expressions, and (4) varying pose. The system performance is also compared with the eigenface method, one of the best face recognition techniques, and with reported experimental results of other methods. A face pre-filtering technique is proposed to speed up the search process. It is a very encouraging to find that the proposed face recognition technique has performed better than the eigenface method in most of the comparison experiments. This research demonstrates that the LEM, together with the proposed generic line-segment Hausdorff distance measure, provides a new method for face coding and recognition.

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.

This paper proposes a novel face representation and recognition method based on local Gabor textons. Textons, defined as a vocabulary of local characteristic features, are a good description of the perceptually distinguishable micro-structures on objects. In this paper, we incorporate the advantages of Gabor feature and textons strategy together to form Gabor textons. And for the specificity of face images, we propose local Gabor textons (LGT) to portray faces more precisely and efficiently. The local Gabor textons histogram sequence is then utilized for face representation and a weighted histogram sequence matching mechanism is introduced for face recognition. Preliminary experiments on FERET database show promising results of the proposed method.Keywordslocal textonsGabor filtershistogram sequenceface recognition

Local binary feature learning has attracted a lot of researches in image recognition due to its vital effectiveness. Generally, in the traditional local feature learning methods, a projection is learned to map the patches of image into binary features and then a codebook is generated by clustering the binary features with K-means clustering. However, these local feature learning methods, such as compact binary face descriptor and discriminative binary descriptor, ignore the category specific distributions of the original features during the feature learning process and use the real-valued clustering approach to generate the codebook, the discriminant of feature is degraded and the merits of binary feature are lost. To tack these problems, in this paper, we propose a novel category-preserving binary feature learning and binary codebook leaning (CPBFL-BCL) method for finger vein recognition. In CPBFL-BCL, the discrimination of learned binary features is generated by criteria of fisher discriminant analysis and category manifold preserving regularity during the feature learning process, and a novel binary clustering method based on K-means clustering is designed to generate binary codebook. Experimental results on recognition and retrieval tasks using two public finger vein databases are presented and demonstrate the effectiveness and efficiency of the proposed method over the state-of-the-art finger vein methods and a finger vein retrieval method.

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 in security applications has driven interesting advancements in this field. In this work, we introduce a new multistep approach for face recognition in the infrared spectrum. The proposed approach works in texture space using binary and ternary pattern descriptors. The approach operates in two steps. In the first step, dimensionality reduction techniques are used to classify the preprocessed infrared face image. This operation permits the selection of the highest score candidates. In the second step, a small set of these candidates are then classified using a correlation based approach. This last step permits the selection of the best matching candidate. The obtained results show a high increase in the face recognition performance when a multistep approach is used compared to dimensionality reduction face recognition techniques alone.