Real Time Face Recognition under Different Conditions

Face recognition is an active area of research since 1980s. It is one of the most successful and important applications of image analysis and processing. Eigenface approach is one of the earliest appearance-based face recognition methods, which was developed by M. Turk and A. Pentland in 1991 [1]. This method utilizes the idea of the Principal Component Analysis (PCA) which decomposes a face image into a small set of characteristic feature images called eigenfaces and recognition is performed by projecting a new face onto a low dimensional linear “face space” defined by the eigenfaces, followed by computing the distance between the resultant position in the face space and those of known face classes. A number of experiments are done at different conditions to evaluate the performance of the face recognition system. The results demonstrate that the face recognition using eigenfaces is quite robust to head/face orientation and illumination.