An algorithm for human-eye localization in images is presented for faces with frontal pose and upright orientation. A given face region is filtered by a highpass wavelet-transform filter. In this way, edges of the region are highlighted, and a caricature-like representation is obtained. Candidate points for each eye are detected after analyzing horizontal projections and profiles of edge regions in the highpass-filtered image. All the candidate points are then classified using a support vector machine. Locations of each eye are estimated according to the most probable ones among the candidate points. It is experimentally observed that our eye localization method provides promising results for image-processing applications.
In this paper, a human face detection method in images and video is presented. After determining possible face candidate
regions using color information, each region is filtered by a high-pass filter of a wavelet transform. In this way, edges of
the region are highlighted, and a caricature-like representation of candidate regions is obtained. Horizontal, vertical and
filter-like projections of the region are used as feature signals in dynamic programming (DP) and support vector machine
(SVM) based classifiers. It turns out that the support vector machine based classifier provides better detection rates
compared to dynamic programming in our simulation studies.