DocumentCode :
1542306
Title :
Studies on Hyperspectral Face Recognition in Visible Spectrum With Feature Band Selection
Author :
Di, Wei ; Zhang, Lei ; Zhang, David ; Pan, Quan
Author_Institution :
Lab. for Applic. of Remote Sensing, Purdue Univ., West Lafayette, IN, USA
Volume :
40
Issue :
6
fYear :
2010
Firstpage :
1354
Lastpage :
1361
Abstract :
This correspondence paper studies face recognition by using hyperspectral imagery in the visible light bands. The spectral measurements over the visible spectrum have different discriminatory information for the task of face identification, and it is found that the absorption bands related to hemoglobin are more discriminative than the other bands. Therefore, feature band selection based on the physical absorption characteristics of face skin is performed, and two feature band subsets are selected. Then, three methods are proposed for hyperspectral face recognition, including whole band (2D)2PCA, single band (2D)2PCA with decision level fusion, and band subset fusion-based (2D)2PCA. A simple yet efficient decision level fusion strategy is also proposed for the latter two methods. To testify the proposed techniques, a hyperspectral face database was established which contains 25 subjects and has 33 bands over the visible light spectrum (0.4-0.72 μm). The experimental results demonstrated that hyperspectral face recognition with the selected feature bands outperforms that by using a single band, using the whole bands, or, interestingly, using the conventional RGB color bands.
Keywords :
face recognition; image colour analysis; principal component analysis; visual databases; RGB color bands; decision level fusion strategy; face skin physical absorption characteristics; feature band selection; hyperspectral face database; hyperspectral face recognition; visible light bands; visible spectrum; Biometrics; Electromagnetic wave absorption; Face detection; Face recognition; Hyperspectral imaging; Hyperspectral sensors; Principal component analysis; Remote sensing; Skin; Testing; Band selection; face recognition; hyperspectral imaging; principal component analysis (PCA);
fLanguage :
English
Journal_Title :
Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on
Publisher :
ieee
ISSN :
1083-4427
Type :
jour
DOI :
10.1109/TSMCA.2010.2052603
Filename :
5512681
Link To Document :
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