Face Recognition Based on PCA, DCT, DWT and Distance Measure

Face Recognition has been identified as one of the attracting research areas and it has drawn the attention of many researchers due to its varying applications such as identity authentication, security access control human computer interaction and surveillance. In this paper, we compare different features extraction algorithms like Principal Component Analysis (PCA), Discrete Cosine Transform (DCT), and Discrete Wavelet Transform (DWT) with different types of distance measures such as Euclidean distance, Cosine distance and Correlation distance. The proposed methods are tested on ORL and Yale Face Databases. These methods are successfully applied to face-recognition, and the experimental results on ORL database gave the good results. we found that the DWT 3rd level decomposition method is the best method with the Euclidean Distance (above 95.33% recognition rate), and the PCA gives the better results with Cosine distance and Correlation distance, The overall results show that the using of DWT method is useful for recognition.