Wavelet transform application in human face recognition

A wavelet transformation is introduced as a new method to extract sideview face features in human face recognition. Utilizing the wavelet transformation a sideview profile is decomposed, as high frequency and low frequency components. Signal reconstruction autocorrelation and energy distribution are used to decide a optimal decomposition level in the wavelet transformation without loosing sideview features. To evaluate the feasibility of the wavelet transformation features in human sideview face recognition, the tie statistic is used to compute the complexity of the wavelet transform features. Using wavelet transformation the sideview data size is reduced. The reduced features have almost the same ability as the original sideview face profile data in terms of distinguishing different people. The computational expense is greatly decreased. The results of the experiments are also shown in this paper.