A Kind Of Face Recognition Algorithm Of Supervision And Two-Way Features Fusion

Face identification is considered as the focus of biometric identification because of its characteristics of being direct, friendly and non-invasive. However, the high dimensionality of face images, and the need for more space for information storage lead to the importance of facial feature extraction. Feature extraction refers to the extraction of the most distinctive face images which are used for identification. This paper is a research on the typical LPP algorithm of feature extraction algorithm and its improved version. LPP is an unsupervised algorithm for 1-dimensional vector feature extraction, which may have the problem of "small sample" and excessively high dimension in the process of. SLPP algorithm and 2DLPP algorithm are two improved versions of LPP algorithm. SLPP algorithm solved the unsupervised problem of LPP algorithm and 2DLPP algorithm for the direct processing of 2-dimensional face image avoids the problem that may occur in the transformation process. But there are problems of SLPP algorithm on constructing the neighbor graph of within-class and between-class. 2DLPP algorithm has the limitation of being unable to retain the overall face feature. The paper try to make an improvement in the above two algorithms respectively, then put forward a new supervised two-way fusion face recognition algorithm that combines the two algorithms. Compared with other face recognition algorithm, the new algorithm is proved to be of higher recognition rate and robustness after being tested by experiments in the standard face database.This paper studies LPP and two improved versions of it, of which the major contributions can be summarized in the following three aspects.First, there are problems of SLPP algorithm on constructing the neighbor graph of within-class and between-class. We propose a new method to construct the graph between-class, which solve the above problem. We prove the consistency of the proposed ESLPP by number of experiments, and then prove that a SLPP algorithm change is reasonable.Secondly, it put forwards the two-way feature fusion algorithm-2DDLPP based on the traditional 2DLPP algorithm, in which the primitive face images are mapped to the horizontal and vertical, two different feature spaces to get two complementary features of face images. The fusion of these two types of facial features well retains the overall characteristics of the human face and distinctive information. More importantly the improved 2DDLPP algorithm is acknowledged to be valid through experiment.Thirdly, the newly proposed 2DDESLPP algorithm solves the problem of "small sample" and excessively high dimension and it is proved that the method has higher recognition rate and robustness.