Independent Component Analysis of Edge Information for Face Recognition under Variation of Pose and Illumination

This paper addresses the problem of face recognition under variation of illumination and poses with large rotation angles using edge information as Independent Component (ICs). The edge information is obtained by using Laplacian of Gaussian (LoG) and second order differential edge detection methods. Then pre-processing is done by using Principle Component analysis (PCA) before applying the Independent Component Analysis (ICA) algorithm for training of images. The independent components obtained by ICA algorithm are used as feature vectors for classification. There are two classifier used for testing of the images. The variation in illumination and facial poses up to 1800 rotation angle is used by the proposed method and result shows that the recognition improved significantly.