Face Detection and Recognition Using Skin Color and AdaBoost Algorithm Combined with Gabor Features and SVM Classifier
Alireza Tofighi, S. Amirhassan Monadjemi · 2011
This paper proposed a method to enhance the performance of face detection and recognition systems. Our method basically consists of two main parts: firstly, we detect faces and then recognize the detected faces. In detection step we used the skin color segmentation with Gaussian skin color model combined with AdaBoost algorithm, which is fast and also more accurate compared to the other known methods. Also, we use a series of morphological operators to improve the face detection performance. Recognition part consists of four steps: Gabor features extraction, dimension reduction using PCA, feature selection using LDA, and SVM based classification. Combination of PCA and LDA is used for improving the capability of LDA when a few samples of images are available. We test the system on the face databases. Experimental results show that system is robust enough to detect faces in different lighting conditions, scales, poses, and skin colors from various races. Also, system is able to recognize face with less misclassification compared to the previous methods.