Research Of Face Detection Based On AD-AaaBoost Algorithm

Face recognition technology is an important part of biometric technology. It has become a cross-hot issue in applied mathematics, pattern recognition and computer science. Face detection technology as a key to face recognition technology, has a very high academic and practical value.This paper summarizes and analyzes the classical algorithm for face detection. The training process and testing process of the AD-AdaBoost algorithm is optimized. A cascade classifier and a human face detection system are designed. The content are described as follows:Firstly, according to the weight update rule in AD-AdaBoost algorithm, a weight update threshold for each loop is given. It can effectively avoid the degeneration issues of overfitting and distortion of sample weights in training process.Secondly, according to Haar-like features, the detecting image size are unchanged, and the detection window are expanded step by step, so that the detection speed have been greatly improved.Finally, the trained face detector was tested in the Chinese Academy of Sciences on the CAS-PEAL-R1database. The detection rate is98%and the false detection rate0.01%.