Facial feature extraction using PCA and wavelet multi-resolution images

This work presents an algorithm for the extraction of the facial feature (eyebrow, eye, nose and mouth) fields from 2-D gray-level face images. The fundamental philosophy is that eigenfeatures, derived from the eigenvalues and eigenvectors of the gray-level data set constructed from the feature fields, are very useful to locate these fields efficiently. In addition, multi-resolution images, derived from a 2-D DWT (Discrete Wavelet Transform), are used to save the search time of the facial features. The experimental results indicate that the proposed algorithm is robust against facial feature size and slight variations of pose.