Color Local Texture Features Based Face Recognition

Abstract-For the purpose of face recognition (FR), the new color local texture features, i.e., color local Gabor wavelets (CLGWs) and color local binary pattern (CLBP), are being proposed. The proposed color local texture features are able to exploit the discriminative information derived from spatiochromatic texture patterns of different spectral channels within a certain local face region. This method encodes the discriminative features by combining both color and texture information as well as its fusion approach. To make full use of both color and texture information, the opponent color texture features are used. The opponent features capture the spatial correlation between spectral bands and taken into the generation of CLGW and CLBP. In addition, to perform the final classification, multiple color local texture features (each corresponding to the associated color band) are combined within a feature-level fusion framework. Particularly, compared with gray scale texture features, the proposed color local texture features are able to provide excellent recognition rates for face images taken under severe variation in illumination, as well as some variations in face images

Face recognition using multiple features in different color spaces

Face recognition as a particular problem of pattern recognition has been attracting substantial attention from researchers in computer vision, pattern recognition, and machine learning. The recent Face Recognition Grand Challenge (FRGC) program reveals that uncontrolled illumination conditions pose grand challenges to face recognition performance. Most of the existing face recognition methods use gray-scale face images, which have been shown insufficient to tackle these challenges. To overcome this challenging problem in face recognition, this dissertation applies multiple features derived from the color images instead of the intensity images only. First, this dissertation presents two face recognition methods, which operate in different color spaces, using frequency features by means of Discrete Fourier Transform (DFT) and spatial features by means of Local Binary Patterns (LBP), respectively. The DFT frequency domain consists of the real part, the imaginary part, the magnitude, and the phase components, which provide the different interpretations of the input face images. The advantage of LBP in face recognition is attributed to its robustness in terms of intensity-level monotonic transformation, as well as its operation in the various scale image spaces. By fusing the frequency components or the multi-resolution LBP histograms, the complementary feature sets can be generated to enhance the capability of facial texture description. This dissertation thus uses the fused DFT and LBP features in two hybrid color spaces, the RIQ and the VIQ color spaces, respectively, for improving face recognition performance. Second, a method that extracts multiple features in the CID color space is presented for face recognition. As different color component images in the CID color space display different characteristics, three different image encoding methods, namely, the patch-based Gabor image representation, the multi-resolution LBP feature fusion, and the DCT-based multiple face encodings, are presented to effectively extract features from the component images for enhancing pattern recognition performance. To further improve classification performance, the similarity scores due to the three color component images are fused for the final decision making. Finally, a novel image representation is also discussed in this dissertation. Unlike a traditional intensity image that is directly derived from a linear combination of the R, G, and B color components, the novel image representation adapted to class separability is generated through a PCA plus FLD learning framework from the hybrid color space instead of the RGB color space. Based upon the novel image representation, a multiple feature fusion method is proposed to address the problem of face recognition under the severe illumination conditions. The aforementioned methods have been evaluated using two large-scale databases, namely, the Face Recognition Grand Challenge (FRGC) version 2 database and the FERET face database. Experimental results have shown that the proposed methods improve face recognition performance upon the traditional methods using the intensity images by large margins and outperform some state-of-the-art methods

Innovative local texture descriptors with application to eye detection

Local Binary Patterns (LBP), which is one of the well-known texture descriptors, has broad applications in pattern recognition and computer vision. The attractive properties of LBP are its tolerance to illumination variations and its computational simplicity. However, LBP only compares a pixel with those in its own neighborhood and encodes little information about the relationship of the local texture with the features. This dissertation introduces a new Feature Local Binary Patterns (FLBP) texture descriptor that can compare a pixel with those in its own neighborhood as well as in other neighborhoods and encodes the information of both local texture and features. The features encoded in FLBP are broadly defined, such as edges, Gabor wavelet features, and color features. Specifically, a binary image is first derived by extracting feature pixels from a given image, and then a distance vector field is obtained by computing the distance vector between each pixel and its nearest feature pixel defined in the binary image. Based on the distance vector field and the FLBP parameters, the FLBP representation of the given image is derived. The feasibility of the proposed FLBP is demonstrated on eye detection using the BioID and the FERET databases. Experimental results show that the FLBP method significantly improves upon the LBP method in terms of both the eye detection rate and the eye center localization accuracy. As LBP is sensitive to noise especially in near-uniform image regions, Local Ternary Patterns (LTP) was proposed to address this problem by extending LBP to three-valued codes. However, further research reveals that both LTP and LBP achieve similar results for face and facial expression recognition, while LTP has a higher computational cost than LBP. To improve upon LTP, this dissertation introduces another new local texture descriptor: Local Quaternary Patterns (LQP) and its extension, Feature Local Quaternary Patterns (FLQP). LQP encodes four relationships of local texture, and therefore, it includes more information of local texture than the LBP and the LTP. FLQP, which encodes both local and feature information, is expected to perform even better than LQP for texture description and pattern analysis. The LQP and FLQP are applied to eye detection on the BioID database. Experimental results show that both FLQP and LQP achieve better eye detection performance than FLTP, LTP, FLBP and LBP. The FLQP method achieves the highest eye detection rate

A Novel Hybrid Feature Extraction Technique For Face Recognition

Face recognition is a computer for identifying and retrieving desired images from a large collection on the basis of features(color, texture, shape..).CBIR system is generally used in security, medicine, entertainment…etc. Interesting method for dimensionality reduction is subspace analysis. Images are portioned into sub images by using sub band decomposition or orthogonal transform to detect local features. Shape feature of an image can be represented using various moments. Image representation using moments has desirable properties of rotation invariance, robust to noise, expression efficiency, effective ness, and multi level representation for describing various shapes of patterns. Further in this work different distance measure like Minkowski distance, Manhatten distance, Euclidean distance etc. will be used to test the performance of the proposed method

Local Binary Patterns applied to Face Detection and Recognition

Nowadays, applications in the field of surveillance, banking and multimedia equipment are becoming more important, but since each application related to face analysis demands different requirements on the analysis process, almost all algorithms and approaches for face analysis are application dependent and a standardization or generalization is quite difficult. For that reason and since many key problems are still not completely solved, the face analysis research community is still trying to cope with face detection and recognition challenges. Although emulating human vision system would be the ideal solution, it is a heuristic and complicated approach which takes into account multiple clues such as textures, color, motion and even audio information. Therefore, and due to the fast evolution of technology that makes it possible, the recent trend is moving towards multimodal analysis combining multiple approaches to converge to more accurate and satisfactory results. Contributions to specific face detection and recognition applications are helpful to enable the face analysis research community to continue building more robust systems by concatenating different approaches and combining them. Therefore, the aim of this research is to contribute by exploring the Local Binary Patterns operator, motivated by the following reasons. On one hand, it can be applied to face detection and recognition and on the other hand due to its robustness to pose and illumination changes. Local Binary Patterns were first used in order to describe ordinary textures and, since a face can be seen as a composition of micro textures depending on the local situation, it is also useful for face description. The LBP descriptor consists of a global texture and a local texture representation calculated by dividing the image into blocks and computing the texture histogram for each one. The global is used for discriminating the most non-face objects (blocks), whereas the second provides specific and detailed face information which can be used not only to select faces, but also to provide face information for recognition. The results will be concatenated in a general descriptor vector, that will be later used to feed an adequate classifier or discriminative scheme to decide the face likeness of the input image or the identity of the input face in case of face recognition. It is in that stage where this research will focus, first evaluating more simple classification methods and then trying to improve face detection and recognition ratios by trying to eliminate features vector redundancy