Research On Two-dimensional Feature Extraction Methods

Face recognition is an important branch of biometrics technology, feature extraction is an important aspect of face recognition, a good or bad method of feature extraction is directly related to the final result of recognition effect. The main concern of traditional feature extraction is one-dimensional research, Based on the analysis of the existing two-dimensional feature extraction methods, the paper focuses on two-dimensional feature fusion algorithm and two-dimensional sparse preserving projection algorithm. The main work is as follows:(1)Based on two-dimensional locality preserving projection algorithm and two-dimensional linear discriminant analysis, This paper fuse two feature sets of two algorithm and the paper proposed a new fusion algorithm called 2DCLPP under the theory of canonical correlation analysis. In addtion, the paper proposed some suggestions for improvement.(2) Based on locality preserving projection algorithm, the paper proposed two-dimensional locality preserving projection algorithm (2DSPP). The algorithm can effectively extract features and resolve the problem of singular covariance matrix. In addtion, the step of two-dimensional sparse representation is necessary in 2DSPP. It proved that the sparse coefficient of two-dimensional sparse representation is the same as one dimension in this paper. So it is easier to find the sparse coefficient for two-dimensional sparse representation.Finally, to prove the effectiveness of the 2DCLPP algorithm and the 2DSPP algorithm, the paper demonstrat the different experiments on the ORL database, AR database and Extended Yale database respectively. Experiments show that the 2DCLPP is an effective two-dimensional feature fusion algorithm, features of it can be effectively used for classification; 2DSPP is an effective two-dimensional feature extraction algorithm and it can get a good recognition effect.