Incorporating Image Quality in Multimodal Biometric Verification

Authors:
Hong Huang;Jianwei Li;Zezhong Ma;Hailiang Feng
Affiliations:
Key Lab. on Opto-electronic Technique and systems, Ministry of Education, Chongqing University, 400030 Chongqing, China;Key Lab. on Opto-electronic Technique and systems, Ministry of Education, Chongqing University, 400030 Chongqing, China;Chongqing Institute of Surveying and Planning for Land, Jiangbei, 400020 Chongqing, China;Key Lab. on Opto-electronic Technique and systems, Ministry of Education, Chongqing University, 400030 Chongqing, China
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Year:
2007

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Abstract

The effect of image quality on the performance of multimodal biometric verification is studied. A biometric system based solely on single modality is often not able to meet the system performance requirements for poor image quality. Prior studies of multimodal biometric authentication have shown that it can improve performance over use of a single unimodal biometric. The well-known multimodal methods do not consider the quality information of the data used when combining the results from different matchers. In the paper, a novel SVM-based multimodal biometric authentication system is presented. It is based on SVM classifiers and quality measures of the input biometric signals. Experimental results on a prototype application based on fingerprint and face are reported. The proposed scheme is shown to outperform significantly multimodal systems without considering quality signals and unimodal systems over a wide range of image quality.