Research Of Face Detection Based On Skin Color Segmentation And PCA Support Vector Novelty Detection

Recently, the identity authentication technology based on biometric has been developed rapidly. Face detection as an important application of biometric authentication technology has been paid more and more attentions. Face detection plays a very important role and practical value in the visual image processing, human-computer interaction, facial expression recognition, video surveillance, content-based search, etc. Many face detection algorithms have been proposed. Because of the complexity of the face, it is difficult to find an algorithm, which can adapt to all kinds of conditions of detection task. More research is based on a variety of effective combination of the algorithms to improve the detection performance of the algorithms. In this paper, the description and analysis about the advantages and disadvantages in current main face detection algorithms has been given. A face detection method combined with the skin color detection and the novel cascade support vector (SVND) is implemented.First, face detection algorithm based on skin color is studied. Through the analyis about clustering of skin color in different color space, we select the H-CbCr color components of the color model for simple division. Then the Gaussian model is set up in the YCbCr color space of simple skin color model segmentation area for secondary division, and the edge detection is added in the process of color segmentation, thus large areas of communicating color or skin color is separated. During the binarization of image, choose the improved between-cluster variance (Otsu) threshold segmentation method for binary, by filtering, morphological processing, in order to eliminate the influence of noise. Face candidate region is better splited up at last.Then, the face method based on SVND is studied and analyzed. Principle of the two classification support vector machine (SVM) method and one-class SVM detection method are introduced in details. The cascade SVND face detection algorithm is realized. The pixel values of the sample image for training and testing will lead to the increasing of the dimension of the sample and the complexity of the calculation. In order to solve this problem, the principal component analysis (PCA) is applied to sample image firstly in this article. Based on the MIT face database and real images, for a cascade SVND, we give the performance comparisons for different characteristic (Pixel features and PCA features) and verify the PCA dimensionality reduction in the effectiveness of face detection.Finally, for the characteristics of cascade SVND based PCA feature and skin detection in face detection, this paper combines these two methods for face detection. Improvement in the detection rate of face detection and reduction in the false detection rate and detection time are verified through experiments. In order to make full use of the skin color segmentation results, the size of cascade SVND detection window is adjusted and the detection time is reduced further.