Facial recognition using new LBP representations

In this paper, we propose a facial recognition based on the LBP operator. We divide the face into non-overlapped regions. After that, we classify a training set using each region at a time under different configurations of the LBP operator. Regarding to the best recognition rate, we consider a weight and specific LBP configuration to the regions. To represent the face image, we extract LBP histograms with the specific configuration (radius and neighbors) and concatenate them into feature histogram. We propose a multi-resolution approach, to gather local and global information and improve the recognition rate. To evaluate our proposed approach, we considered the FERET data set, which includes different facial expressions, lighting, and aging of the subjects. In addition, weighted Chi-2 is considered as a dissimilarity measure. The experimental results show a considerable improvement against the original idea

A Study on Facial Expression Recognition Using Local Binary Pattern

How to get the proper combination of feature extraction and classification is still crucial in facial expression recognition, and it has been addressed conducted over two decades. Hence, if inadequate features are used, even the best classifier could fail to achieve the accurate recognition. Therefore, Local Binary Pattern (LBP) is used as a feature extraction technique for facial expressions recognition where it is evaluated based on statistical local features. LBP is proven successful technique by the recent study due to its speed and discrimination performance aside of robust to low-resolution images. For the classification, Support Vector Machine is chosen, and the algorithm is implemented in MATLAB and tested on JAFFE (Japanese Female Facial Expressions) database in order to achieve the objectives and the goal of this research which is to obtain high accuracy in facial expressions and identify the seven basic facial expressions. The performance of feature extraction and classification is evaluated based on the recognition accuracy. The observation on results obtained in facial expressions recognition rate indicated the effectiveness of the proposed algorithm based on SVM-LBP features

FUZZY BINARY PATTERNS FOR UNCERTAINTY-AWARE TEXTURE REPRESENTATION

The Local Binary Pattern (LBP) representation of textures has been proved useful for a wide range of pattern recognition applications, including texture segmentation, face detection, and biomedical image analysis. The interest of the research community in the LBP texture representation gave rise to plenty of LBP and other binary pattern (BP)-based variations. However, noise sensitivity is still a major concern to their applicability on the analysis of real world images. To cope with this problem we propose a generic, uncertainty-aware methodology for the derivation of Fuzzy BP (FBP) texture models. The proposed methodology assumes that a local neighbourhood can be partially characterized by more than one binary patterns due to noise-originated uncertainty in the pixel values. The texture discrimination capability of four representative FBP-based approaches has been evaluated on the basis of comprehensive classification experiments on three reference datasets of natural textures under various types and levels of additive noise. The results reveal that the FBP-based approaches lead to consistent improvement in texture classification as compared with the original BP-based approaches for various degrees of uncertainty. This improved performance is also validated by illustrative unsupervised segmentation experiments on natural scenes

Face recognition in uncontrolled conditions using sparse representation and local features

Face recognition in presence of either occlusions, illumination changes or large expression variations is still an open problem. This paper addresses this issue presenting a new local-based face recognition system that combines weak classifiers yielding a strong one. The method relies on sparse approximation using dictionaries built on a pool of local features extracted from automatically cropped images. Experiments on the AR database show the effectiveness of our method, which outperforms current state-of-the art techniques

The effects of Pose on Facial Expression Recognition

Research into facial expression recognition has predominantly been based upon near frontal view data. However, a recent 3D facial expression database (BU-3DFE database) has allowed empirical investigation of facial expression recognition across pose. In this paper, we investigate the effects of pose from frontal to profile view on facial expression recognition. Experiments are carried out on 100 subjects with 5 yaw angles over 6 prototypical expressions. Expressions have 4 levels of intensity from subtle to exaggerated. We evaluate features such as local binary patterns (LBPs) as well as various extensions of LBPs. In addition, a novel approach to facial expression recognition is proposed using local gabor binary patterns (LGBPs). Multi class support vector machines (SVMs) are used for classification. We investigate the effects of image resolution and pose on facial expression classification using a variety of different features