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A robust probabilistic collaborative representation based classification for multimodal biometrics
Author(s): Jing Zhang; Huanxi Liu; Derui Ding; Jianli Xiao
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Paper Abstract

Most of the traditional biometric recognition systems perform recognition with a single biometric indicator. These systems have suffered noisy data, interclass variations, unacceptable error rates, forged identity, and so on. Due to these inherent problems, it is not valid that many researchers attempt to enhance the performance of unimodal biometric systems with single features. Thus, multimodal biometrics is investigated to reduce some of these defects. This paper proposes a new multimodal biometric recognition approach by fused faces and fingerprints. For more recognizable features, the proposed method extracts block local binary pattern features for all modalities, and then combines them into a single framework. For better classification, it employs the robust probabilistic collaborative representation based classifier to recognize individuals. Experimental results indicate that the proposed method has improved the recognition accuracy compared to the unimodal biometrics.

Paper Details

Date Published: 10 April 2018
PDF: 8 pages
Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 106151F (10 April 2018); doi: 10.1117/12.2302763
Show Author Affiliations
Jing Zhang, Univ. of Shanghai for Science and Technology (China)
Huanxi Liu, Shanghai Jiao Tong Univ. (China)
Derui Ding, Univ. of Shanghai for Science and Technology (China)
Jianli Xiao, Univ. of Shanghai for Science and Technology (China)


Published in SPIE Proceedings Vol. 10615:
Ninth International Conference on Graphic and Image Processing (ICGIP 2017)
Hui Yu; Junyu Dong, Editor(s)

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