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Research Article Open Access
Palmprint is a promising biometric feature for use in access control and forensic departments. Already availed researches on palmprint recognition mainly concentrates on low-resolution (about 100ppi) palmprints. But for highsecurity applications forensic usage), high-resolution palmprints (500ppi or higher) are required from which more useful quality information can be extracted. In this paper, we introduce a novel- recognition algorithm for very high-resolution image. The main contributions of the proposed algorithm include the following: 1) use of multiple features namely minutiae, principle lines, density and orientation, palmprint recognition to significantly improve the matching performance of the conservative algorithm. 2) Design of a quality-based and adaptive orientation field estimation algorithm which performs better than the present algorithm in case of regions with a large number of creases. 3) Use of a novel-fusion algorithm for an detection application which performs better than conservative fusion methods, e.g., SVMs, Neyman- Pearson rule or weighted sum rule. Besides, we analyze the discriminative influence of different characteristic combinations and find that concreteness that is very useful for palmprint recognition. Experimental outcome on the database containing 14,576 palmprints show that the proposed algorithm has achieved a good piece. In the case of verification, the recognition system’s False Rejection Rate (FRR) is 16 percent, which is 17 percent lesser than the best existing algorithm at a False Acceptance Rate (FAR) of 10^-5, while in the credential experiment, the rank-1 live-scan fractional palmprint recognition rate is enhanced from 82.0 to 91.7 percent.