Fast face detection via morphology-based pre-processing1

Abstract

An efficient face detection algorithm which can detect multiple faces in cluttered environment is proposed. First of all, morphological operations and labeling process were performed to obtain the eye-analogue segments. Based on some matching rules and the geometrical relationship on a face, eye-analogue segments were grouped into pairs and used to locate potential face regions. Finally, the potential face regions were verified via a trained neural network and the true faces were determined by optimizing a distance function. Since the morphology-based eye-analogue segmentation process can efficiently locate the potential eye-analogue regions, the subsequent processing only has to deal with 5-10% area of the original image. Experiments demonstrate that an approximately 94% success rate is reached and the relative false detection rate is very low.