Human face recognition using PCA on wavelet subband

Together with the growing interest in the development of human and computer interface and biometric identification, human face recognition has become an active research area since early 90. Nowadays, Principal Component Analysis (PCA) has been widely adopted as the most promising face recognition algorithm. Yet still, PCA has its limitations: poor discriminatory power and large computational load. In view of these limitations, this paper proposed a new approach in using PCA apply PCA on wavelet subband. Traditionally, to represent the human face, PCA is performed on the whole facial image. In the proposed method, wavelet transform is used to decompose an image into different frequency subbands, and a mid-range frequency subband is used for PCA representation. In comparison with the traditional use of PCA, the proposed method gives better recognition accuracy and discriminatory power; further, the proposed method reduces the computational load significantly when the image database is large, with more than 256 training images. This paper details the design and implementation of the proposed method, and presents the encouraging experimental results.