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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 different types of voting techniques for the proposed system.
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fig12: Results of different types of voting techniques for the proposed system.

Mentions: In nash voting technique, each voter assigns a number between zero and one for each candidate and then compares the product of the voter's values for all the candidates. The highest is the winner:(10)Q(x)=arg max⁡j=1N∏i=1Kyij.Different types of voting techniques, that is, average vote, maximum vote, nash vote, and majority vote, have been applied to measure the accuracy of the proposed system. Figure 12 shows the comparison results of different types of voting techniques. Though the recognition rates are very close to each other of the voting techniques, majority voting technique gives the highest recognition rate of the proposed system.


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

Islam MR - Comput Intell Neurosci (2014)

Results of different types of voting techniques for the proposed system.
© Copyright Policy
Related In: Results  -  Collection

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

fig12: Results of different types of voting techniques for the proposed system.
Mentions: In nash voting technique, each voter assigns a number between zero and one for each candidate and then compares the product of the voter's values for all the candidates. The highest is the winner:(10)Q(x)=arg max⁡j=1N∏i=1Kyij.Different types of voting techniques, that is, average vote, maximum vote, nash vote, and majority vote, have been applied to measure the accuracy of the proposed system. Figure 12 shows the comparison results of different types of voting techniques. Though the recognition rates are very close to each other of the voting techniques, majority voting technique gives the highest recognition rate of the proposed system.

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