Face recognition based on PCA on wavelet subband

Many recent events, such as terrorist attacks, exposed serious weaknesses in most sophisticated security systems, it is necessary to improve security data systems based on the body or behavioral characteristics, often called biometrics. 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. Face recognition appears to offer several advantages over other biometric methods. Nowadays Principal Component Analysis (PCA) has been widely adopted for the face recognition algorithm. Yet still, PCA has its limitations such as poor discriminatory power and large computational load. In this paper to improve the performance of PCA, it is applied on Daubechies wavelet subbands. Results are compared using City Block distance and Euclidean distance measures. The best recognition rate is obtained using PCA on subband A3 of db2 wavelet using City block distance measure.