Color Face Detection Based On Support Vector Machine Technology

Face detection has been a research focus of computer vision and pattern recognition for a long time. It also has the application in a wide range of fields such as automatic face recognition systems, visual surveillance, content-based retrieval, advanced human and computer interaction. But, due to the complexity of the purpose, most face detection methods have the weakness of large computation, low efficiency and many false reports among the detection result.Support Vector Machines (SVM) is a new pattern recognition technology that is established on Statistical Learning Theory. It can solve small-sample learning problems better by using Experiential Risk Minimization. Moreover, this theory can change the problem in non-linearity space to that in the linearity space in order to reduce the algorithm complexity by using the kernel function idea. However, SVM theory performance has been validated in many practical applications, there are still some drawbacks. For example: train speed is slow, algorithm is complex and check phase operation is large, etc.This paper proposed a new method to detect frontal view human faces in color images quickly. This dissertation adopting background separate technique with skin color detection technique combine together to get the area which may be have person's face, then adoption SVM method to proceed detection. Because of the area is small, this kind of method namely guaranteed the detection rate at the same time detections peed that near to the real time. SVM-Based Face Detection made of training section and detecting section. A lot of face samples and "not face" samples are used to train the SVM classifier, to get optimal separating hyper plane in the training. And SVM classifier is used to detect faces in the detecting.Examination in some image databases prove this method have veryhigh detection rate, and have the very high theories value and practical value.