Face–iris multimodal biometric scheme based on feature level fusion

Guang Huo; Yuanning Liu; Xiaodong Zhu; Hongxing Dong; Fei He

doi:10.1117/1.JEI.24.6.063020

24 December 2015 Face–iris multimodal biometric scheme based on feature level fusion

Guang Huo, Yuanning Liu, Xiaodong Zhu, Hongxing Dong, Fei He

Author Affiliations +

Journal of Electronic Imaging, Vol. 24, Issue 6, 063020 (December 2015). https://doi.org/10.1117/1.JEI.24.6.063020

Abstract

Unlike score level fusion, feature level fusion demands all the features extracted from unimodal traits with high distinguishability, as well as homogeneity and compatibility, which is difficult to achieve. Therefore, most multimodal biometric research focuses on score level fusion, whereas few investigate feature level fusion. We propose a face–iris recognition method based on feature level fusion. We build a special two-dimensional-Gabor filter bank to extract local texture features from face and iris images, and then transform them by histogram statistics into an energy-orientation variance histogram feature with lower dimensions and higher distinguishability. Finally, through a fusion-recognition strategy based on principal components analysis and support vector machine (FRSPS), feature level fusion and one-to-n identification are accomplished. The experimental results demonstrate that this method can not only effectively extract face and iris features but also provide higher recognition accuracy. Compared with some state-of-the-art fusion methods, the proposed method has a significant performance advantage.

Citation Download Citation

Guang Huo, Yuanning Liu, Xiaodong Zhu, Hongxing Dong, and Fei He "Face–iris multimodal biometric scheme based on feature level fusion," Journal of Electronic Imaging 24(6), 063020 (24 December 2015). https://doi.org/10.1117/1.JEI.24.6.063020

Published: 24 December 2015

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

Includes PDF, HTML & Video, when available