Fast and robust face detection with skin color mixture models and asymmetric AdaBoost

Abstract

We present a new approach to face detection with skin color mixture models and asymmetric AdaBoost. First, non-skin color pixels of the input image are rapidly removed based on skin color mixture models in RGB and YCbCr chrominance spaces, from which we extract candidate face regions. Then, face detection with fast asymmetric AdaBoost is carried out in candidate face regions where ratios of pixels of skin color to non-skin color are beyond certain thresholds. To further reduce the computational cost, the integral image technique is employed to calculate ratios of pixels of skin color to non-skin color in candidate face regions. Finally, false alarms are gradually merged and removed by relative geometric relation and the rate of skin color pixels on the intersection line of candidate face regions. Experimental results show that our proposed method reduces significantly false alarms and the processing time while achieves detection rates of more than 99%.