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Multimodal biometrics approach using face and ear recognition to overcome adverse effects of pose changes

[+] Author Affiliations
Yu Wang

Beijing Technology and Business University, School of Computer and Information Engineering, Beijing 100048, China

Dejian He

Altar Technology Co. Ltd., Beijing 10080, China

Chongchong Yu

Beijing Technology and Business University, School of Computer and Information Engineering, Beijing 100048, China

Tongqiang Jiang

Beijing Technology and Business University, School of Computer and Information Engineering, Beijing 100048, China

Zaiwen Liu

Beijing Technology and Business University, School of Computer and Information Engineering, Beijing 100048, China

J. Electron. Imaging. 21(4), 043026 (Dec 27, 2012). doi:10.1117/1.JEI.21.4.043026
History: Received March 10, 2012; Revised August 28, 2012; Accepted October 16, 2012
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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 happens.

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© 2012 SPIE and IS&T

Topics

Ear ; Biometrics

Citation

Yu Wang ; Dejian He ; Chongchong Yu ; Tongqiang Jiang and Zaiwen Liu
"Multimodal biometrics approach using face and ear recognition to overcome adverse effects of pose changes", J. Electron. Imaging. 21(4), 043026 (Dec 27, 2012). ; http://dx.doi.org/10.1117/1.JEI.21.4.043026


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