Based On The Integrated Face Recognition

Face recognition plays an important role in the development and application of pattern recognition. At present, it is an active research topic. A general statement of the problem can be formulated as three steps: face detection, facial feature extraction, match and recognition. This paper deals with all above three steps, most work is described as follows:(1) We present a method of face detection based on subspace feature. In this paper, K-L transformation is used to get subspace of eigenfaces. Then projection coefficients on the subspace can be obtained. Based on this, blur cluster and weighted k-nearest neighbor algorithm are adopted to detect faces, which greatly enhances the speed of face detection.(2) In face recognition based on subspace feature, we propose local NMF for extraction of subspace of eigenfaces in order to solve the problem that NMF can't extract local feature. Our method can fully extract local feature of image, which is helpful in enhancing recognition rate. Meanwhile, Bagging algorithm is used, which improves classification accuracy and generalization of neural network.(3) In face recognition based on geometrical feature, we present a method of localization in order to overcome the problem that eyes can't be located accurately. Firstly we localize eyes according to geometrical feature, then we use eigeneyes template to validate the result.(4) We present a metasynthesis method of face recognition, considering that subspace and geometrical feature reflect different feature information. In metasynthesis, we present a method of deciding dynamically estimator of weights based on neural network and genetic algorithm, in order to overcome the problem that weights are decided unskillfully.The experimental results prove that recognition rate is higher basedon our methods than that based on individual geometrical feature or individual subspace feature.