Abstract
A major approach for palmprint recognition today is to extract feature vectors corresponding to individual palmprint images and to perform palmprint matching based on some distance metrics. One of the difficult problems in feature- based recognition is that the matching performance is significantly influenced by many parameters in feature extraction process, which may vary depending on environmental factors of image acquisition. This paper presents a palmprint recognition using Zernike moments feature extraction. Unsharp filtered palmprint images makes possible to achieve highly robust palmprint recognition. Experimental evaluation using a palmprint image database clearly demonstrates an efficient matching performance of the proposed system.
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