10 April 2018 A robust probabilistic collaborative representation based classification for multimodal biometrics
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Proceedings Volume 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017); 106151F (2018) https://doi.org/10.1117/12.2302763
Event: Ninth International Conference on Graphic and Image Processing, 2017, Qingdao, China
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.
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Jing Zhang, Huanxi Liu, Derui Ding, Jianli Xiao, "A robust probabilistic collaborative representation based classification for multimodal biometrics", Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 106151F (10 April 2018); doi: 10.1117/12.2302763; https://doi.org/10.1117/12.2302763
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