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Journal of Electronic Imaging

Multimodal biometrics approach using face and ear recognition to overcome adverse effects of pose changes
Author(s): Yu Wang; Dejian He; Chongchong Yu; Zaiwen Liu
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Paper Abstract

A personal identification method is proposed which uses face and ear together to overcome mass information loss resulting from pose changes. Several aspects are mainly considered: First, ears are at both sides of the face. Their physiological position is approximately orthogonal and their information is complementary to each other when the head pose changes. Therefore, fusing the face and ear is reasonable. Second, the texture feature is extracted using a uniform local binary pattern (ULBP) descriptor which is more compact. Third, Haar wavelet transform, blocked-based, and multiscale ideas are taken into account to further strengthen the extracted texture information. Finally, texture features of face and ear are fused using serial strategy, parallel strategy, and kernel canonical correlation analysis to further increase the recognition rate. Experimental results show that it is both fast and robust to use ULBP to extract texture features. Haar wavelet transform, block-based, and multiscale methods can effectively enhance texture information of the face or ear ULBP descriptor. Multimodal biometrics fusion about face and ear is feasible and effective. The recognition rates of the proposed approach outperform remarkably those of the classic principal component analysis (PCA), kernel PCA, or Gabor texture feature extraction method especially when sharp pose change

Paper Details

Date Published: 27 December 2012
PDF: 12 pages
J. Electron. Imaging. 21(4) 043026 doi: 10.1117/1.JEI.21.4.043026
Published in: Journal of Electronic Imaging Volume 21, Issue 4
Show Author Affiliations
Yu Wang, Beijing Technology and Business Univ. (China)
Dejian He, Altar Technology Co., Ltd. (China)
Chongchong Yu, Beijing Technology and Business Univ. (China)
Zaiwen Liu, Beijing Technology and Business Univ. (China)

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