EFFECTIVE FACE RECOGNITION THROUGH LOCAL TEXTURE FEATURES A. Malcom Marshall1 and Dr.S.Gunasekaran2 1 M.E-Computer Science and Engineering CIET-Coimbatore malcom.research@gmail.com 2 Prof and Head- Computer Science and Engineering CIET-Coimbatore gunaphd@yahoo.com ABSTRACT The new color local texture features that means color local Gabor wavelets (CLGWs) and color local binary pattern (CLBP), for the purpose of face recognition (FR). In this method able to provide excellent recognition rates for face images taken under severe variation in illumination, as well as for small- (low-) resolution face images. In addition, the feasibility of our color local texture features has been successfully demonstrated by making comparisons with other state-of-the-art color FR methods. The color local texture features consists of three major steps: color space conversion and partition, feature extraction, and combination and classification. So to allows for a significant improvement in the FR accuracy when recognizing face images taken under a severe change in illumination (at least, in the data sets used in our experimentation), as well as for low-resolution face images, as compared with their grayscale counterparts. In addition, comparative experimental results show that color FR methods using color local texture features yield better or comparable FR performance compared with those obtained using other recent advanced color FR methods. Color local texture method do not easy to recognize the face and if variation in face means do not get proper results. So using LDA approach to overcome the above problem. LDA is a statistical approach for classifying samples of unknown classes based on training samples with known classes. This technique aims to maximize between-class (i.e., across users) variance and minimize within-class (i.e., within user) variance. In each block represents a class, there are large variances between classes, but little variance within classes. When dealing with high dimensional face data, this technique faces the small sample size problem that arises where there are a small number of available training samples compared to the dimensionality of the sample space. Keywords: Color faces recognition (FR), Color local texture features, Combination, Color spaces, Gabor wavelets, Local binary pattern. INTRODUCTION: Face recognition (FR) has received a significant interest in pattern recognition and computer vision due to the wide range of applications including video surveillance, biometric identification, and face indexing in multimedia contents. As in any classification task, feature extraction is of great importance in the FR process. Recently, local texture features have gained reputation as powerful face descriptors because they are believed to be more robust to variations of facial pose, expression, occlusion, etc. In particular, Gabor wavelets and local binary pattern (LBP) texture features have proven to be highly discriminative for FR due to different levels of locality. In Three grayscale texture techniques including local linear transform, Gabor filtering, and co-occurrence methods are extended to color images. This paper reports that the use of color information can improve classification performance obtained using only grayscale texture analysis techniques. In incorporating color into a texture analysis can be beneficial for classification recognition schemes. In particular, the authors showed that perceptually uniform color spaces and YCbCr for color texture analysis. Following the aforementioned studies, it is natural to expect