A Face Detection Method Based on Skin Color Features and AdaBoost Algorithm

Abstract

The face detection based on skin color features is greatly affected by illumination and the face detection based on AdaBoost algorithm is greatly affected by multi-pose of face, and this means that the large face deflection will make the detection less accurate. So we investigated a face detection method that combines the skin color model and the improved AdaBoost algorithm in this paper. This method first judges whether the collected face image needs illumination compensation. The image is converted from RGB color space to YCbCr color space with higher pixel clustering characteristics and distribution rules by whether it is needed or not. Meanwhile, the candidate regions containing face images are carried out through morphological process and shape select after establishing the Gaussian model. Finally, the face detection results are obtained through detecting the candidate regions with face images by improved AdaBoost algorithm. A large number of experimental results show that this new method can effectively reduce the false detection rate and improve the efficiency of face detection.