Learning-based Face Detection by Adaptive Switching of Skin Color Models and AdaBoost under Varying Illumination
Deng-Yuan Huang, Chun-Jih Lin, Wu-Chih Hu · J. Inf. Hiding Multim. Signal Process. · 2011
Face detection has a variety of applications, such as face recognition, face expression analysis, and video conferencing. However, most existing methods for face detection are sensitive to lighting variation. In this paper, a novel scheme invariant with illumination based on adaptive switching of skin color models (ASSM) with lighting compensation for face detection is proposed. The skin-tone pixels detected are connected by the proposed fast 8-connected component labeling method into a more compact skin cluster. An optimal skin color model is thus adaptively selected using a well-dened quality measure. Possible face candidates are further validated by a cascaded AdaBoost detector. Experimental results indicate that robust face detection can be achieved for various lighting conditions, such as dim light, side light, and back light. A detection time of 60 ms for each frame is achieved with the aid of the ASSM method. A detection rate of 94.4% was obtained for a test video sequence.