Feature Extraction Based on Self-Adaptive ICA in Face Detection
Liqing Zhang · Jisuanji fangzhen · 2007
It is still a hard problem to extract efficient features from images to distinguish faces and non-face images.The paper presents a new approach to extract facial features from plenty of frontal face images with self-adaptive ICA algorithm which is sensitive to image structures so that face images can be represented efficiently by the projections on these facial features.It is an advantage of self-adaptive ICA algorithm to estimate image statistics without any assumptions in advance.Face and non-face images can be classified well via comparing their projections on the facial features.Computer simulations show that a set of good facial features can be found by the proposed approach.The comparison with Boosted Cascaded method also shows that weak classifiers employing the proposed approach yield 1% ～ 1.5% higher classification performances.