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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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Feature encoding and dimensionality reduction process.
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fig4: Feature encoding and dimensionality reduction process.

Mentions: For extracting the feature, it is important to extract the most important information presented in the iris region. There are different alternative techniques that can be used for feature extraction which includes Gabor filtering techniques, Zero-crossing 1D wavelet filters, Log-Gabor filters, and Haar wavelet. In this work, Log-Gabor filtering technique has been applied to extract the iris features effectively. 9600 feature values have been taken from each iris region. Principal Component Analysis method has been used to reduce the dimension of the feature vector where 550 feature values have been taken. Feature extraction and dimensionality reduction process are shown in Figure 4.


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

Islam MR - Comput Intell Neurosci (2014)

Feature encoding and dimensionality reduction process.
© Copyright Policy
Related In: Results  -  Collection

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

fig4: Feature encoding and dimensionality reduction process.
Mentions: For extracting the feature, it is important to extract the most important information presented in the iris region. There are different alternative techniques that can be used for feature extraction which includes Gabor filtering techniques, Zero-crossing 1D wavelet filters, Log-Gabor filters, and Haar wavelet. In this work, Log-Gabor filtering technique has been applied to extract the iris features effectively. 9600 feature values have been taken from each iris region. Principal Component Analysis method has been used to reduce the dimension of the feature vector where 550 feature values have been taken. Feature extraction and dimensionality reduction process are shown in Figure 4.

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