earticle

영어

Feature extraction is the most crucial part of face recognition system, which has important role in the field of pattern recognition. There are a lot of classic algorithms about feature extraction at present, such as the method based on linear and nonlinear. An effective classification and dimension reduction method, local embedded graph attribute selection algorithm with maximal region is to generate the inherent graph and penalty graph, to overcome the low efficiency problem of local linear embedding (LLE) method and maximum margin criterion (MMC) method. With inherent picture, the structured nonlinear can be found on the high-dimensioned space by using local geometry of the restructured linear, which leads to the same instances gathering together as more as possible. Meanwhile, different class instances are as far as possible from each other in penalty picture. Since LLE is an unsupervised method, not enhance visual clustering classification ability, so compact figure within the class to consider the sample class information, can sample the same category as compact. In this method, the smallest size instance issue was tackled by the employment of MMC and the neighborhood relationship can be better described by an adequate improvement of the adjacency matrix. The effectiveness of algorithm proposed by this paper is present by a large amount of experiences in the standard face databases of Yale, AR and ORL facial data.