Abstract
In this paper, a novel unconstrained correlation filter called Unconstrained Optimal Origin Tradeoff Filter (UOOTF) is presented and applied to face recognition. Compared with the conventional correlation filters in Class-dependence Feature Analysis (CFA), UOOTF increases the overall performance for unseen patterns by removing the hard constraints on the outputs during the filter design. Experimental results on the popular FERET, FRGC and CAS-PEAL R1 face databases show the effectiveness of the proposed unconstrained correlation filter. © Springer-Verlag 2013.
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CITATION STYLE
Yan, Y., Wang, H., Li, C., Yang, C., & Zhong, B. (2013). A novel unconstrained correlation filter and its application in face recognition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7751 LNCS, pp. 32–39). https://doi.org/10.1007/978-3-642-36669-7_5
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