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Proceedings Paper

Adaptive SVM fusion for robust multi-biometrics verification with missing data
Author(s): Xiuna Zhai; Yan Zhao; Jingyan Wang; Yongping Li
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Paper Abstract

Conventional multimodal biometrics systems usually do not account for missing data (missing modalities or incomplete score lists) that is commonly encountered in real applications. The presence of missing data in multimodal biometric systems can be inconvenient to the client, as the system will reject the submitted biometric data and request for another trial. In such cases, robust multimodal biometric verification is needed. In this paper, we present the criteria, fusion method and performance metrics of a robust multimodal biometrics verification system that verifies the client’s identity at any condition of data missing. A novel adaptive SVM classification method is proposed for missing dimensional values, which can handle the missing data in multimodal biometrics. We show that robust multibiometrics imposes additional requirements on multimodal fusion when compared to conventional multibiometrics. We also argue that the usual performance metrics of false accept and false reject rates are insufficient yardsticks for robust verification and propose new metrics against which we benchmark our system.

Paper Details

Date Published: 14 March 2013
PDF: 7 pages
Proc. SPIE 8768, International Conference on Graphic and Image Processing (ICGIP 2012), 87682R (14 March 2013); doi: 10.1117/12.2010895
Show Author Affiliations
Xiuna Zhai, North China Institute of Aerospace Engineering (China)
Yan Zhao, North China Institute of Aerospace Engineering (China)
Jingyan Wang, Shanghai Institute of Applied Physics (China)
Yongping Li, Shanghai Institute of Applied Physics (China)

Published in SPIE Proceedings Vol. 8768:
International Conference on Graphic and Image Processing (ICGIP 2012)
Zeng Zhu, Editor(s)

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