An Efficient Illumination Normalization Method with Fuzzy LDA Feature Extractor for Face Recognition

The most significant practical challenge for face recognition is perhaps variability in lighting intensity. In this paper, we developed a face recognition which is insensitive to large variation in illumination. Normalization including two steps, first we used Histogram truncation as a preprocessing step and then we implemented Homomorphic filter. The main idea is that, achieving illumination invariance causes to simplify feature extraction module and increases recognition rate. Then we utilized Fuzzy Linear Discriminant Analysis (FLDA) in feature extraction stage which showed a good discriminating ability compared to other methods while classification is performed using three classification methods : Nearest Neighbour classifier , Support Vector Machines (SVM) and Feedforward Neural Network(FFNN).The experiments were performed on the ORL (Olivetti Research Laboratory) and Yale face image databases and the results show the present method with SVM classifier outweighs other techniques applied on the same database and reported in literature.