Efficient Face/Pose Detection Based on Machine Learning

Abstract

Machine learning is a state-of-the-art scheme in solving many kinds of complicated problems. This paper utilizes two types of machine learning algorithms to detect skin and face/pose respectively. Initially a hierarchical neural network is applied for skin detection. Begin with a neural network to overcome the diversity of light and follow by a second neural network to get over colors near the skin color. After the skin area is detected, an AdaBoost learning algorithm is implemented for face/pose detection. Haar-like features [11][12] are utilized as features of modified Adaboost to determine whether there is a left, frontal, right, or non-face in a 20 x 20 sliding window. Experimental results show that the proposed method achieves a good performance in skin color detection, capacity of coping with the problems of scaling, rotation and multiple faces, as well as a good detection rate.