Classifier selection using sequential error ratio criterion for multi-instance and multi-sample fusion

Nallagatla, Vishnu P. & Chandran, Vinod (2012) Classifier selection using sequential error ratio criterion for multi-instance and multi-sample fusion. In Wysocki, Beata & Wyscoki, Tadeuza A (Eds.) Proceedings of the 6th International Conference on Signal Processing and Communication Systems (ICSPCS'2012), IEEE, Radisson Resort Gold Coast, Gold Coast, QLD, pp. 1-8.

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Abstract

Classifier selection is a problem encountered by multi-biometric systems that aim to improve performance through fusion of decisions. A particular decision fusion architecture that combines multiple instances (n classifiers) and multiple samples (m attempts at each classifier) has been proposed in previous work to achieve controlled trade-off between false alarms and false rejects. Although analysis on text-dependent speaker verification has demonstrated better performance for fusion of decisions with favourable dependence compared to statistically independent decisions, the performance is not always optimal. Given a pool of instances, best performance with this architecture is obtained for certain combination of instances. Heuristic rules and diversity measures have been commonly used for classifier selection but it is shown that optimal performance is achieved for the `best combination performance' rule. As the search complexity for this rule increases exponentially with the addition of classifiers, a measure - the sequential error ratio (SER) - is proposed in this work that is specifically adapted to the characteristics of sequential fusion architecture. The proposed measure can be used to select a classifier that is most likely to produce a correct decision at each stage. Error rates for fusion of text-dependent HMM based speaker models using SER are compared with other classifier selection methodologies. SER is shown to achieve near optimal performance for sequential fusion of multiple instances with or without the use of multiple samples. The methodology applies to multiple speech utterances for telephone or internet based access control and to other systems such as multiple finger print and multiple handwriting sample based identity verification systems.

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ID Code: 61819
Item Type: Conference Paper
Refereed: Yes
Keywords: Classifier selection, Sequential fusion, Multi-instance and multi-sample fusion, Sequential error ratio, Optimal fusion performance
DOI: 10.1109/ICSPCS.2012.6507989
ISBN: 9781467323932
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2012 IEEE.
Copyright Statement: Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this
work in other works must be obtained from the IEEE.
Deposited On: 15 Aug 2013 00:08
Last Modified: 15 Jan 2014 14:48

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