Research On Feature Detection Algorithm In Face Recognition System

In today's information-oriented society, how reliable and effective to identify individuals has become a very important issue. biometric identification technology because of its uniqueness, reliability therefore it has many advantages than traditional methods such as password, ID card, credit. automatic face recognition technology with the advantages of friendship and direct to people therefore is particularly widespread attentioned.Face feature detection is the process of finding face position then locating the human eyes, nose, mouth in the static images or dynamic video frequency that containing face. To achieve fast and accurate facial feature detection is the preconditon of building a high-performance automatic face recognition system. This paper make an in-depth research in the face feather detection algorithm, then it proposes and implements some facial feature tracking methods in a dynamic video camera.Firstly, using face detection algorithm based on adaboost and YCbCr color space achieves a simple, practical method for the human face feature detection and tracking. Adaboost-base face detection algorithm is real-time and accurate, it extracts face haar-like shape feature then uses machine learning algorithm of adaboost to train face detection classifier for face detection, it is the most outstanding face detecting algorithm, then based on the skin color in the YCbCr color space has a good clustering characteristic we achieve a good face feature tracking.Secondly, integrated with active appearance model (AAM) algorithm and adaboost-base face detection algorithm we achieve another reliable and accurate face feature tracking algorithm. Active appearance model (AAM) algorithm applys the principal component analysis (PCA) on the face shape, texture and other overall face characteristics, then we attain a group of model parameters, by adjusting the model parameters we can achieve the precise position of face features. Because the AAM fitting algorithm has a harsh requirements on the initial placing position of the model, so this paper use the adaboost-base face detection algotithm to solve this problem, and eventually we achieve rapid and realible face feature tracking.