Face detection and recognition using skin color

This paper proposes a method to enhance the performance of face detection and recognition systems. This method basically consists of two main parts as detection of faces and then recognizing the detected faces. In detection step, skin color segmentation with thresholding skin color model combined with AdaBoost algorithm is used, which is fast and also more accurate in detecting the faces. Also, a series of morphological operators is used to improve the face detection performance. Recognition part consists of three steps: Gabor features extraction, dimension reduction and feature selection using PCA, and KNN based classification. Testing of the system on different face databases is done. Our aim is to show that system is robust enough to detect faces in different lighting conditions, scales, poses, and skin colors from various races and to recognize face with less misclassification compared to the previous methods.