Human face detection and eye location in video using wavelets

Human face detection and eye localization problems have received significant attention during the past several years because of wide range of commercial and law enforcement applications. In this thesis, wavelet domain based human face detection and eye localization algorithms are developed. After determining all possible face candidate regions using color information in a given still image or video frame, each region is filtered by a high-pass filter of a wavelet transform. In this way, edge-highlighted caricature-like representations of candidate regions are obtained. Horizontal, vertical and filter-like edge projections of the candidate regions are used as feature signals for classification with dynamic programming (DP) and support vector machines (SVMs). It turns out that the proposed feature extraction method provides good detection rates with SVM based classifiers. Furthermore, the positions of eyes can be localized successfully using horizontal projections and profiles of horizontal- and vertical-crop edge image regions. After an approximate horizontal level detection, each eye is first localized horizontally using horizontal projections of associated edge regions. Horizontal edge profiles are then calculated on the estimated horizontal levels. After determining eye candidate points by pairing up the local maximum point locations in the horizontal profiles with the associated horizontal levels, the verification is also carried out by an SVM based classifier. The localization results show that the proposed algorithm is not affected by both illumination and scale changes.