Summary

An efficient face detection algorithm which can detect multiple faces in a cluttered en-vironment is proposed. The proposed system consists of three main steps. In the first step, a morphology-based technique is devised to perform eye-analogue segmentation. In a gray image, the eyebrows, eyes, nostrils and mouth always look darker than the rest of a face. Among these facial features, the eyes can be considered a salient and relatively stable feature than the other facial organs. Morphological operations are applied to locate eye-analogue pixels in the original image. Then, a labeling process is executed to generate the eye-analogue segments. In the second step, the previously located eye-analogue segments are used as guides to search for potential face regions. Suppose that there are M eye-analogue segments in image E; atmostM 2 potential combinations of face regions are generated via the matched eye pairs. In order to reduce the execution time, the geometrical constraint on a real eye pair is introduced to screen out some impossible pairs. Once the potential eye pairs are determined, their corresponding face regions can be easily extracted. The last step of the proposed system is to perform face verification. In this step, every face candidate obtained from the previous step is normalized to a standard size. Then, each of these normalize