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Feature and score fusion based multiple classifier selection for iris recognition.

Islam MR - Comput Intell Neurosci (2014)

Bottom Line: Four Discrete Hidden Markov Model classifiers output, that is, left iris based unimodal system, right iris based unimodal system, left-right iris feature fusion based multimodal system, and left-right iris likelihood ratio score fusion based multimodal system, is combined using voting method to achieve the final recognition result.CASIA-IrisV4 database has been used to measure the performance of the proposed system with various dimensions.Experimental results show the versatility of the proposed system of four different classifiers with various dimensions.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science & Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh.

ABSTRACT
The aim of this work is to propose a new feature and score fusion based iris recognition approach where voting method on Multiple Classifier Selection technique has been applied. Four Discrete Hidden Markov Model classifiers output, that is, left iris based unimodal system, right iris based unimodal system, left-right iris feature fusion based multimodal system, and left-right iris likelihood ratio score fusion based multimodal system, is combined using voting method to achieve the final recognition result. CASIA-IrisV4 database has been used to measure the performance of the proposed system with various dimensions. Experimental results show the versatility of the proposed system of four different classifiers with various dimensions. Finally, recognition accuracy of the proposed system has been compared with existing N hamming distance score fusion approach proposed by Ma et al., log-likelihood ratio score fusion approach proposed by Schmid et al., and single level feature fusion approach proposed by Hollingsworth et al.

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Results of comparison among unimodal left iris, unimodal right iris, and multimodal left-right iris recognition system.
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fig11: Results of comparison among unimodal left iris, unimodal right iris, and multimodal left-right iris recognition system.

Mentions: The integration weight of right iris modality measure can be found as(7)γR=(1−γL).The results after applying score fusion approach for iris recognition system are shown in Figure 11. The results show the comparison among unimodal left iris, unimodal right iris, and left-right iris score fusion based multimodal recognition system. Here, the score fusion approach achieves higher recognition rate than any individual unimodal system of left and right iris.


Feature and score fusion based multiple classifier selection for iris recognition.

Islam MR - Comput Intell Neurosci (2014)

Results of comparison among unimodal left iris, unimodal right iris, and multimodal left-right iris recognition system.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4120484&req=5

fig11: Results of comparison among unimodal left iris, unimodal right iris, and multimodal left-right iris recognition system.
Mentions: The integration weight of right iris modality measure can be found as(7)γR=(1−γL).The results after applying score fusion approach for iris recognition system are shown in Figure 11. The results show the comparison among unimodal left iris, unimodal right iris, and left-right iris score fusion based multimodal recognition system. Here, the score fusion approach achieves higher recognition rate than any individual unimodal system of left and right iris.

Bottom Line: Four Discrete Hidden Markov Model classifiers output, that is, left iris based unimodal system, right iris based unimodal system, left-right iris feature fusion based multimodal system, and left-right iris likelihood ratio score fusion based multimodal system, is combined using voting method to achieve the final recognition result.CASIA-IrisV4 database has been used to measure the performance of the proposed system with various dimensions.Experimental results show the versatility of the proposed system of four different classifiers with various dimensions.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science & Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh.

ABSTRACT
The aim of this work is to propose a new feature and score fusion based iris recognition approach where voting method on Multiple Classifier Selection technique has been applied. Four Discrete Hidden Markov Model classifiers output, that is, left iris based unimodal system, right iris based unimodal system, left-right iris feature fusion based multimodal system, and left-right iris likelihood ratio score fusion based multimodal system, is combined using voting method to achieve the final recognition result. CASIA-IrisV4 database has been used to measure the performance of the proposed system with various dimensions. Experimental results show the versatility of the proposed system of four different classifiers with various dimensions. Finally, recognition accuracy of the proposed system has been compared with existing N hamming distance score fusion approach proposed by Ma et al., log-likelihood ratio score fusion approach proposed by Schmid et al., and single level feature fusion approach proposed by Hollingsworth et al.

Show MeSH