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


Autoria(s): Nallagatla, Vishnu P.; Chandran, Vinod
Contribuinte(s)

Wysocki, Beata

Wyscoki, Tadeuza A

Data(s)

2012

Resumo

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.

Identificador

http://eprints.qut.edu.au/61819/

Publicador

IEEE

Relação

DOI:10.1109/ICSPCS.2012.6507989

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.

Direitos

Copyright 2012 IEEE.

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Fonte

School of Electrical Engineering & Computer Science; Science & Engineering Faculty

Palavras-Chave #Classifier selection #Sequential fusion #Multi-instance and multi-sample fusion #Sequential error ratio #Optimal fusion performance
Tipo

Conference Paper