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

Yu Wang; Dejian He; Chongchong Yu; Zaiwen Liu

doi:10.1117/1.JEI.21.4.043026

27 December 2012 Multimodal biometrics approach using face and ear recognition to overcome adverse effects of pose changes

Yu Wang, Dejian He, Chongchong Yu, Zaiwen Liu

Author Affiliations +

Funded by: National Natural Science Foundation of China, Natural Science Foundation of China, Excellent Talents Training Foundation of Beijing, Research Foundation for Youth Scholars of Beijing Technology and Business University

Journal of Electronic Imaging, Vol. 21, Issue 4, 043026 (December 2012). https://doi.org/10.1117/1.JEI.21.4.043026

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

Citation Download Citation

Yu Wang, Dejian He, Chongchong Yu, and Zaiwen Liu "Multimodal biometrics approach using face and ear recognition to overcome adverse effects of pose changes," Journal of Electronic Imaging 21(4), 043026 (27 December 2012). https://doi.org/10.1117/1.JEI.21.4.043026

Published: 27 December 2012

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE