Principal Local Binary Patterns for Face Representation and Recognition

A comparative study on local binary pattern (LBP) based face recognition: LBP histogram versus LBP image

The Local Binary Pattern (LBP) and its variants are capable of extracting image texture and have been successfully applied to classification. However, LBP has not been used to extract and describe the texture of polarized images, and simple LBP cannot characterize the polarized texture information from different polarizations of angles. In order to solve these problems, we propose a new multi-angle orthogonal difference polarization image texture descriptor (MODP_ITD) by analyzing the relationship between the difference of orthogonal difference polarization images from different angles and the pixel intensity distribution in the local neighborhood of images from different angles. The MODP_ITD consists of three patterns: multi-angle polarization orthogonal difference local binary pattern (MODP_LBP), multi-angle polarization orthogonal difference local sampling point principal component sequence pattern (MODP_LPCSP) and multi-angle orthogonal difference polarization local difference binary pattern (MODP_LDBP). The MODP_LBP extracts local corresponding texture characteristics of polarized orthogonal difference images from multiple angles. The MODP_LPCSP sorts the principal component order of each angle orthogonal difference local sampling point. The MODP LDBP extracts the local difference characteristics between different angles by constructing a new polarized image. Then, the frequency histograms of MODP_LBP, MOD_LPCSP ,and MODP_LDBP are cascaded to generate MODP_ITD, so as to distinguish local neighborhoods. By using vertical and parallel polarization and unpolarized light active illumination, combined with the measurements at three different detection zenith angles, we constructed a polarization texture image database. A substantial number of experimental results on the self-built database show that our proposed MODP_ITD can represent the detailed information of polarization images texture. In addition, compared with the existing LBP methods, The MODP_ITD has a competitive advantage in classification accuracy.

This paper presents a combination techniques of appearance-based and feature-based feature extraction on the T-Zone face area to improve the recognition performance. This study shows that the T-Zone area and the combined technique provides a significant impact on the face recognition rate. A T-Zone face image is first divided into small regions where Local Binary Pattern (LBP) histograms are extracted and then concatenated into a single feature vector. This feature vector will further reduce the dimensionality scope by using the well established Principle Component Analysis (PCA) technique. Experiments have been carried out on the different sets of the Olivetti Research Laboratory (ORL) database. High recognition rates are obtained when compared to other face recognition methods of the same class. Our result shows of 7% improvement compared with PCA and 2% improvement compare with Sub-Holistic PCA. Our studies proves that the T-Zone area which is consisting of eyes and nose region is a ‘significant facial region’, and we also show that LBP can easily be combined with PCA to reduce the length of the feature vector, while the recognition performance is improved.

A face tracking algorithm based on LBP (Local Binary Pattern) histograms and particle filtering is presented in this paper. The mean shift algorithm is adopted to locate the face region coarsely. Then the particles can be propagated efficiently with small random noise added. Since color histograms as face representation can be disturbed easily by the background with skin color. LBP histograms are used as the representation of faces. In order to alleviate the influence of light variance, the face template is updated under certain condition. The experimental results show that the proposed algorithm is superior to the CAMSHIFT algorithm and the particle filter based on color histograms.

In ground-based remote sensing cloud image observation, images with the highest possible resolution are captured to obtain sufficient information about clouds. However, when features are extracted and classification is performed on the basis of the original images, a high-resolution probably means a high (or even more, unacceptable) computation cost. In practical application, a simple and commonly adopted method is to appropriately resize the original image to a version with a decreased resolution. An inevitable problem is whether useful information is lost in this resizing operation. This paper demonstrates that information loss is inevitable and poor classification results may be obtained from the analysis of local binary pattern (LBP) histogram features. However, this problem has been always neglected in previous studies, and the original image is arbitrarily resized without any criterion. In particular, the histogram features based on LBPs actually reflect the distribution of features. Thus, a criterion based on the Kullback–Leibler divergence between LBP histograms from the original and resized images and a penalty term imposed on the resolution are proposed to select the resolution of the resized image. The optimal resolution of the resized image can be selected by minimizing this criterion. Furthermore, experiments based on three ground-based remote sensing cloud image data sets with different original resolutions validate this criterion by analyzing the LBP histogram features.

Face recognition is a process of verifying an individual using facial images and it is widely employed in identifying people on social media platforms, validating identity at ATMs, finding missing persons, controlling access to sensitive areas, finding lost pets, etc. Face recognition is still a trending research area because of various challenges like illumination variations, different poses, and expressions of the person. Here, a novel methodology is introduced for face recognition using Histogram of Oriented Gradients (HOG), histogram of Local Binary Patterns (LBP), and Convolutional Neural Network (CNN). The features from HOG, histogram of LBP, and deep features from the proposed CNN are linearly concatenated to produce the feature space and then classified by Support Vector Machine. The face databases ORL, Extended Yale B, and CMUPIE are used for experimental work and attained a recognition rate of 98.48%, 97.33%, and 97.28% respectively.

Combination the corresponding advantage of infrared and visible images, the fusion of visible-light and near-infrared features is becoming one of the key research directions of uncontrolled face recognition. In this paper, a new face fusion recognition method based on near-infrared and visible-light imaging is proposed to make full use of the complementarity in two modalities. Firstly, for near infrared imaging, the low frequency part is retained for discrete cosine transform (DCT) coefficients and the block histogram of local binary pattern (LBP) is jointly applied to represent the discriminative features. Then, LBP features from visible faces are gotten to make up the incapable of local representation in near-infrared imaging. Finally, the three features are sent to the novel two-state fusion module to leverage the performance of face recognition. The experimental results show that the combination of DCT and LBP can extract the complementary features from infrared and visible images, and the two-stage fusion strategy lifts the robustness of the multi-modality face recognition. Especially, the recognition rate of the proposed fusion recognition algorithm has been improved significantly, with regard to the other methods based on statistical feature fusion under the circumstance of small samples.

Visible face recognition systems, being vulnerable to illumination, expression, and pose, can not achieve robust performance in unconstrained situations. Meanwhile, near infrared face images, being light- independent, can avoid or limit the drawbacks of face recognition in visible light, but its main challenges are low resolution and signal noise ratio (SNR). Therefore, near infrared and visible fusion face recognition has become an important direction in the field of unconstrained face recognition research. In this paper, a novel fusion algorithm in non-subsampled contourlet transform (NSCT) domain is proposed for Infrared and visible face fusion recognition. Firstly, NSCT is used respectively to process the infrared and visible face images, which exploits the image information at multiple scales, orientations, and frequency bands. Then, to exploit the effective discriminant feature and balance the power of high-low frequency band of NSCT coefficients, the local Gabor binary pattern (LGBP) and Local Binary Pattern (LBP) are applied respectively in different frequency parts to obtain the robust representation of infrared and visible face images. Finally, the score-level fusion is used to fuse the all the features for final classification. The visible and near infrared face recognition is tested on HITSZ Lab2 visible and near infrared face database. Experiments results show that the proposed method extracts the complementary features of near-infrared and visible-light images and improves the robustness of unconstrained face recognition.

This paper presents a novel face recognition method by using the new image representations. While the commonly used gray-scale image is derived from the linear combination of R, G, and B color component images, the new image representations are derived from the Principal Component Analysis (PCA) tranform upon the hybrid configurations of different color component images. Compared to the correlated color space RGB, the correlations in other configurations of color components (such as RCrQ, YIQ, YCbQ, and so on) are reduced and hence the diversities among their misclassification outputs are enhanced. The new image representations, which inherit advantages from all the individual color components, are thus more invariable than the gray-scale image to the image variations for the face recognition task. Furthermore, we propose to encode the facial information from the new image representations by using an effective Local Binary Pattern (LBP) feature extraction method, which extracts and fuses the multi-resolution LBP features. Finally, the resulting LBP features undergo the Fisher discriminant analysis for face recognition. The most challenging Face Recognition Grand Challenge (FRGC) version 2 Experiment 4 shows the proposed method, which achieves the face verification rate of 83.41% at the false accept rate of 0.1%, performs better than some recent face recognition methods.

Biometrics have been widely used for human identification, including fingerprint, face and iris because they cannot be easily duplicated. Recently, biometrics have been also used for animal (i.e. cattle in this paper) identification. Individual cattle identification is necessary for many important reasons including determining legal ownership, verifying transferred source, and implementing disease surveillance and control. Popular traditional methods for individual cattle identification are using plastic ear tags or microchips. However, the tag can be deduplicated and it can be dangerous and takes time for the human expert to place the microchip in the cattle. Also, it may hurt the cattle. Thus, in this paper, muzzle print is used as a biometric for automatic cattle identification. The fusion of texture features extracted from the muzzle image is used to represent individual cattle. They are Gabor feature and Local Binary Pattern (LBP) histogram. Gabor features were extracted at the different scales and orientations in specific frequencies, while LBP histogram was extracted for each local sub-image to preserve local spatial textures. Then, Support Vector Machine (SVM) is employed as a classifier. The proposed method is reported with the perfect accuracy.

This paper proposes a novel face representation approach, local Gabor binary mapping pattern (LGBMP), for multi-view gender classification. In this approach, a face image is first represented as a series of Gabor magnitude pictures (GMP) by applying multi-scale and multi-orientation Gabor filters. Each GMP is then encoded as a LGBP image where a uniform local binary pattern (LBP) operator is used. After that, each LGBP image is divided into non-overlapping rectangular regions, from which spatial histograms are extracted. Although an LGBP feature vector can be obtained by fitting together the regional histograms, it can not be employed in pattern classification due to its high dimension. We propose that each regional LGBP feature be mapped onto a one-dimensional subspace independently before they are concatenated as a whole feature vector. This is attractive since we reduce the feature dimension and also preserve the spatial information of LGBP image. Two ways have been proposed to map the regional LGBP feature in this paper. One is so-called LGBMP-LDA using linear discriminant analysis (LDA) for dimensionality reduction while the other is to project the regional LGBP feature onto the class center connecting line, namely, LGBMP-CCL. As a result, despite several decades of Gabor filters, the final feature dimension is even less than that of the feature extracted by using LBP directly on gray-scale images. The classification tasks in our work are performed by support vector machines (SVM). The experimental results on the CAS-PEAL face database indicate that the proposed approach achieves higher accuracy than the others such as SVMs+Gray-scale pixel, SVMs+Gabor and SVMs+LBP approach, more particularly, it has the lowest dimension of feature vector.

In this work, our objective is to study the impact of statistical hypothesis tests for the purpose of improving heterogeneous face recognition (FR). A series of tests are conducted to find the most suitable type of statistical analysis test (parametric vs. non-parametric). To conduct the experiments, we used a multi-spectral face database (visible and Near-IR) collected under challenging conditions, i.e. at night time and at four different standoff distances, namely 30, 60, 90 and 120 meters. Next, the selected statistical analysis test is used to find the statistical significance of; (i) image restoration, (ii) fusion of scores. First, Gabor Wavelets, Histogram of gradients (HOG) and Local binary patterns (LBP) feature descriptors are empirically selected. Then the statistical analysis reveals which descriptors result in higher recognition performance. Finally, statistical hypothesis tests are performed to explore the impact of data stratification (grouping of gallery and probe sets) in terms of ethnicity, gender. A set of face identification studies are performed. Experimental results suggest that our proposed image restoration approach, fusion schemes and the usage of stratification result in a significantly better performance results than the baseline, e.g. the rank-one score is improved from 50% to 71% when using image restoration, to 73% when using fusion of scores and to 75% (i.e. in the case of testing FR accuracy only on the female Asian class) when employing database stratification.

The initial concept behind the system is to create a secure and powerful smart voting mechanism that incorporates the use of face recognition, the algorithm used being Local Binary Pattern Histogram (LBPH) and Aadhar identification, and use of OTPs to authenticate voters. The main idea behind this is to modernize the voting process removing the errors in it that were made manually, stop fraud in identity identification and allow participating in the process regardless of location. The LBPH which has a reputation of being simple and efficient in real-time face recognition transforms the facial images into local binary patterns subsequently lending them to the creation of histograms which depict features of distinct faces. The training phase involves taking several faces of registered voters in case of different lighting conditions and pose conditions. These pictures are scanned to black and white, they are pre-processed and marked with respective voter identities. The voting is then done in the trained LBPH model to authenticate the voter upon real-time webcam feedback where the system detects the face process it and match it with the trained database. Upon successful verification of the face, the system further cross verifies the Aadhar details of the voter and sends him a one-time password (OTP) to his/her recorded phone number. The voter is then allowed into the candidate panel after the successful multi-factor authentication, where he or she votes. Votes are hash stored in a blockchain where each confirmed vote is hashed with the secure hash, SHA-256, providing the requirement of vote recording that is hashing, traceable, and immutable. Such a multi-tiered solution does not only provide a better class of security and transparency, but also fosters accessibility in long-distance voters, thus the proposed system is a strong and highly scalable answer to the current democratic procedures.

The conventional local binary pattern (LBP) based facial recognition method selects Chi square statistic as the dissimilarity measure for LBP histogram.In view of Chi square statistic's complexity and the high-dimensional recognition process,the conventional method is very slow as recognizing on large-scale face database.A method of LBP histogram projection (LBPHP) was proposed,which projects LBP histogram onto locality preserving projection (LPP) space and obtains low-dimensional LBPHP feature.Recognizing new sample only needs to compare its LBPHP feature with those of training samples.The process is simple and carried on low-dimensional space,thus the proposed method is fast and has good accuracy in the light of powerful discriminative property of LPP.Comparative experiments on two large-scale face databases demonstrated that the LBPHP method is superior to the conventional method on recognition speed.The LBPHP method is prominent especially on large-scale face database and suitable for practical application,e.g.identity authentication.

Facial expression which carries rich information of body behavior is the leading carrier of human affective and the symbol of intelligence. The main purpose of this paper is to recognize 3D human facial expression. The research in this paper includes the expression feature extraction algorithm and fusion with different kinds of feature. To contain more local texture feature information, we proposed a new feature of 3D facial expression named Local Threshold Binary Pattern (LTBP) which based on Local Binary Pattern (LBP). We calculate the difference of gray value standard between neighboring pixels and the center pixel as a threshold to binary instead of the traditional LBP operation which only comparison of size between neighboring pixels and the center pixel. After we get the LTBP feature, we fuse the LTBP and HOG (Histogram of Oriented Gradient) features to get multi-feature fusion for 3D facial expression recognition. Our algorithm of 3D facial expression recognition comprises three steps: (1) extracting two sets of feature vectors and establishing the correlation criterion function between the two sets of feature vectors; (2) solving the two sets canonical projective vectors and extracting their canonical correlation features by the framework of canonical correlation analysis algorithm; (3) doing feature fusion for classification by using proposed strategy. We have performed comprehensive experiments on the BU-3DFE database which is presently the largest available 3D face database. We have achieved verification rates of more than 90% for the 3D facial expression recognition.