Authors: Jingyan Wang, Yongping Li, Chao Wang
Addresses: Shanghai Institute of Applied Physics, Chinese Academy of Science, 2019 Jialuo Road, Jiading District, Shanghai 201800, China. ' Shanghai Institute of Applied Physics, Chinese Academy of Science, 2019 Jialuo Road, Jiading District, Shanghai 201800, China. ' OGI School of Science & Engineering, Oregon Health & Science University (OHSU), Beaverton, Oregon, 97006, USA
Abstract: Conventional multimodal biometrics systems usually do not account for missing modalities that is commonly encountered in real applications. 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 Support Vector Machine (SVM) classification method is proposed for missing dimensional values. We 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.
Keywords: multi-biometrics; missing data robustness; performance metrics; adaptive SVM; single vector machines; multimodal biometrics; verification; SVM classification.
International Journal of Biometrics, 2011 Vol.3 No.3, pp.265 - 283
Received: 17 Jun 2010
Accepted: 09 Dec 2010
Published online: 24 Jan 2015 *