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Intra-Class Threshold Generation in Multimodal Biometric Systems by Set Estimation Technique

Intra-Class Threshold Generation in Multimodal Biometric Systems by Set Estimation Technique

Dhiman Karmakar, Madhura Datta, C.A. Murthy
Copyright: © 2013 |Volume: 5 |Issue: 3 |Pages: 11
ISSN: 1942-9045|EISSN: 1942-9037|EISBN13: 9781466633865|DOI: 10.4018/ijssci.2013070102
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MLA

Karmakar, Dhiman, et al. "Intra-Class Threshold Generation in Multimodal Biometric Systems by Set Estimation Technique." IJSSCI vol.5, no.3 2013: pp.22-32. http://doi.org/10.4018/ijssci.2013070102

APA

Karmakar, D., Datta, M., & Murthy, C. (2013). Intra-Class Threshold Generation in Multimodal Biometric Systems by Set Estimation Technique. International Journal of Software Science and Computational Intelligence (IJSSCI), 5(3), 22-32. http://doi.org/10.4018/ijssci.2013070102

Chicago

Karmakar, Dhiman, Madhura Datta, and C.A. Murthy. "Intra-Class Threshold Generation in Multimodal Biometric Systems by Set Estimation Technique," International Journal of Software Science and Computational Intelligence (IJSSCI) 5, no.3: 22-32. http://doi.org/10.4018/ijssci.2013070102

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Abstract

Biometric recognition techniques attracted the researchers for the last two decades due to their many applications in the field of security. In recent times multimodal biometrics have been found to perform better, in several aspects, over unimodal biometrics. The classical approach for recognition is based on dissimilarity measure and for the sake of proper classification one needs to put a threshold on the dissimilarity value. In this paper an intra-class threshold for multimodal biometric recognition procedure has been developed. The authors' selection method of threshold is based on statistical set estimation technique which is applied on a minimal spanning tree and consisting of fused face and iris images. The fusion is performed here on feature level using face and iris biometrics. The proposed method, applied on several multimodal datasets, found to perform better than traditional ROC curve based threshold technique.

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