Name
Abstract | ||
|---|---|---|
In this paper, we propose a very simple and fast face recognition method and present its potential rationale. This method first selects only the nearest training sample, of the test sample, from every class and then expresses the test sample as a linear combination of all the selected training samples. Using the expression result, the proposed method can classify the testing sample with a high accuracy. The proposed method can classify more accurately than the nearest neighbor classification method (NNCM). The face recognition experiments show that the classification accuracy obtained using our method is usually 2-10% greater than that obtained using NNCM. Moreover, though the proposed method exploits only one training sample per class to perform classification, it might obtain a better performance than the nearest feature space method proposed in Chien and Wu (IEEE Trans Pattern Anal Machine Intell 24:1644-1649, 2002), which depends on all the training samples to classify the test sample. Our analysis shows that the proposed method achieves this by modifying the neighbor relationships between the test sample and training samples, determined by the Euclidean metric. © 2012 Springer-Verlag London Limited. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/s00521-012-0833-5 | Neural Computing and Applications |
Keywords | DocType | Volume |
FISHER DISCRIMINANT-ANALYSIS,PCA,CLASSIFICATION | Journal | 22 |
Issue | ISSN | Citations |
7-8 | 0941-0643 | 26 |
PageRank | References | Authors |
0.72 | 14 | 2 |