Abstract:

Nearest Feature Space Embedding(NFSE) algorithm uses traditional Euclidean distance measure when choosing the nearest feature spaces in  the training phase, which causes within-class scatters and between-class scatters change synchronously. The nearest neighborhoodmatching rule also uses Euclidean distance measure in the matching phase, but straight-line distances among samples in higher space are almost the same. They both can reduce the recognition rate. In order to solve this problem, this paper proposes a nearest feature space embedding method based on the combination of nonlinear distance metric and included angle (NL-IANFSE). In the training phase, NL-IANFSE brings nonlinear distance measure to make the change rate of within-class scatter much slower than that of between-class scatter so that distances of samples within same class are  smaller and distances of samples belong to different classes are  larger in the transformed space.In the matching phase, NL-IANFSE uses the nearest neighbor classifier that combines Euclidean distance and included anglebetween two samples，takes the relationship between similarity of samples and included angles of samples into account，and hence is more suitablefor sample classification in high-dimensional space.Experimental results show that the proposed method outperforms the other algorithms in terms of samples classification in high dimensional space.

Key words: face recognition, nonlinear distance；included angle；nearest feature space embedding, Laplacian face