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Optical Engineering

Robust classification for occluded ear via Gabor scale feature-based non-negative sparse representation
Author(s): Baoqing Zhang; Zhichun Mu; Chen Li; Hui Zeng
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Paper Abstract

The Gabor wavelets have been experimentally verified to be a good approximation to the response of cortical neurons. A new feature extraction approach is investigated for ear recognition by using scale information of Gabor wavelets. The proposed Gabor scale feature conforms to human visual perception of objects from far to near. It can not only avoid too much redundancy in Gabor features but also tends to extract more precise structural information that is robust to image variations. Then, Gabor scale feature-based non-negative sparse representation classification (G-NSRC) is proposed for ear recognition under occlusion. Compared with SRC in which the sparse coding coefficients can be negative, the non-negativity of G-NSRC conforms to the intuitive notion of combing parts to form a whole and therefore is more consistent with the biological modeling of visual data. Additionally, the use of Gabor scale features increases the discriminative power of G-NSRC. Finally, the proposed classification paradigm is applied to occluded ear recognition. Experimental results demonstrate the effectiveness of our proposed algorithm. Especially when the ear is occluded, the proposed algorithm exhibits great robustness and achieves state-of-the-art recognition performance.

Paper Details

Date Published: 2 December 2013
PDF: 12 pages
Opt. Eng. 53(6) 061702 doi: 10.1117/1.OE.53.6.061702
Published in: Optical Engineering Volume 53, Issue 6
Show Author Affiliations
Baoqing Zhang, Univ. of Science and Technology Beijing (China)
Zhichun Mu, Univ. of Science and Technology Beijing (China)
Chen Li, North China Univ. of Technology (China)
Hui Zeng, Univ. of Science and Technology Beijing (China)

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