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INTEGRATING IMAGE QUALITY IN 2ν-SVM BIOMETRIC MATCH SCORE FUSION

Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV 26506–6109, USA

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RICHA SINGH

Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV 26506–6109, USA

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, and

AFZEL NOORE

Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV 26506–6109, USA

Corresponding author.

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https://doi.org/10.1142/S0129065707001196Cited by:23 (Source: Crossref)

Abstract

This paper proposes an intelligent 2ν-support vector machine based match score fusion algorithm to improve the performance of face and iris recognition by integrating the quality of images. The proposed algorithm applies redundant discrete wavelet transform to evaluate the underlying linear and non-linear features present in the image. A composite quality score is computed to determine the extent of smoothness, sharpness, noise, and other pertinent features present in each subband of the image. The match score and the corresponding quality score of an image are fused using 2ν-support vector machine to improve the verification performance. The proposed algorithm is experimentally validated using the FERET face database and the CASIA iris database. The verification performance and statistical evaluation show that the proposed algorithm outperforms existing fusion algorithms.

Keywords:

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