DC Field | Value | Language |
---|---|---|
dc.contributor.author | Choi, Jae Young | - |
dc.contributor.author | Ro, Yong Man | - |
dc.contributor.author | Plataniotis, Konstantinos N. | - |
dc.date.accessioned | 2011-07-14T01:07:59Z | - |
dc.date.available | 2011-07-14T01:07:59Z | - |
dc.date.issued | 2011-07-14 | - |
dc.identifier.uri | http://hdl.handle.net/10203/24619 | - |
dc.description.abstract | This paper introduces the new color face recognition (FR) method that makes effective use of boosting learning as color-component feature selection framework. The proposed boosting color-component feature selection framework is designed for finding the best set of color-component features from various color spaces (or models), aiming to achieve the best FR performance for a given FR task. In addition, to facilitate the complementary effect of the selected color-component features for the purpose of color FR, they are combined using the proposed weighted feature fusion scheme. The effectiveness of our color FR method has been successfully evaluated on the following five public face databases (DBs): CMU-PIE, Color FERET, XM2VTSDB, SCface, and FRGC 2.0. Experimental results show that the results of the proposed method are impressively better than the results of other state-of-the-art color FR methods over different FR challenges including highly uncontrolled illumination, moderate pose variation, and small resolution face images. | en |
dc.description.sponsorship | This study was executed as a part of the Research and Development Project of the Archives Preservation Technology hosted and supported of the National Archives of Korea, the Ministry of Public Administration and Security, for which we would like to extend our sincere gratitude. The authors would also like to thank the FERET Technical Agent, the US National Institute of Standards and Technology (NIST) for providing the FERET database. In addition, portions of the research in this paper use the SCface database of facial images. Credit is hereby given to the University of Zargreb, Faculty of Electrical Engineering and Computing for providing the database of facial images. | en |
dc.language.iso | en_US | en |
dc.subject | Color face recognition | en |
dc.subject | boosting learning | en |
dc.subject | color space | en |
dc.subject | color-component | en |
dc.subject | feature selection | en |
dc.subject | weighted feature fusion | en |
dc.title | Boosting Color Feature Selection for Color Face Recognition | en |
dc.type | Article | en |
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