Sequential fusion of decisions from adaptive and random samples for controlled verification of errors

Nallagatla, Vishnu P. & Chandran, Vinod (2012) Sequential fusion of decisions from adaptive and random samples for controlled verification of errors. In Proceedings of 11th International Conference on Information Science, Signal Processing and their Applications, Montreal, Canada, pp. 793-798.

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Abstract

Fusion techniques have received considerable attention for achieving performance improvement with biometrics. While a multi-sample fusion architecture reduces false rejects, it also increases false accepts. This impact on performance also depends on the nature of subsequent attempts, i.e., random or adaptive. Expressions for error rates are presented and experimentally evaluated in this work by considering the multi-sample fusion architecture for text-dependent speaker verification using HMM based digit dependent speaker models. Analysis incorporating correlation modeling demonstrates that the use of adaptive samples improves overall fusion performance compared to randomly repeated samples. For a text dependent speaker verification system using digit strings, sequential decision fusion of seven instances with three random samples is shown to reduce the overall error of the verification system by 26% which can be further reduced by 6% for adaptive samples. This analysis novel in its treatment of random and adaptive multiple presentations within a sequential fused decision architecture, is also applicable to other biometric modalities such as finger prints and handwriting samples.

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ID Code: 50764
Item Type: Conference Paper
Refereed: Yes
Keywords: fusion techniques, biometrics, text-dependent speaker verification, multi-sample fusion architecture
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2012 Please consult the author
Deposited On: 10 Jul 2012 02:46
Last Modified: 20 Feb 2013 09:30

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