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. 2015:2015:861629.
doi: 10.1155/2015/861629. Epub 2015 Jun 14.

Personal Authentication Using Multifeatures Multispectral Palm Print Traits

Gayathri Rajagopal  1 , Senthil Kumar Manoharan  1
Affiliations

Personal Authentication Using Multifeatures Multispectral Palm Print Traits

Gayathri Rajagopal et al. ScientificWorldJournal. 2015.

Abstract

Biometrics authentication is an effective method for automatically recognizing a person's identity with high confidence. Multispectral palm print biometric system is relatively new biometric technology and is in the progression of being endlessly refined and developed. Multispectral palm print biometric system is a promising biometric technology for use in various applications including banking solutions, access control, hospital, construction, and forensic applications. This paper proposes a multispectral palm print recognition method with extraction of multiple features using kernel principal component analysis and modified finite radon transform. Finally, the images are classified using Local Mean K-Nearest Centroid Neighbor algorithm. The proposed method efficiently accommodates the rotational, potential deformations and translational changes by encoding the orientation conserving features. The proposed system analyses the hand vascular authentication using two databases acquired with touch-based and contactless imaging setup collected from multispectral Poly U palm print database and CASIA database. The experimental results clearly demonstrate that the proposed multispectral palm print authentication obtained better result compared to other methods discussed in the literature.

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Figures

Figure 1
Figure 1
Image enhancement: (a) original ROI image; (b) enhanced image.
Figure 2
Figure 2
Block diagram for personal identification using multispectral palm print biometric system.
Figure 3
Figure 3
Feature extraction: (a) KPCA feature using Gaussian mapping, (b) restructured image of (a).
Figure 4
Figure 4
(a) Cropped and canny image and (b) MFRAT feature extraction.
Figure 5
Figure 5
Classification result using LMKNCN.
Figure 6
Figure 6
Receiver operating characteristics from the CASIA database: (a) left palm, (b) right palm, and (c) left and right palm.
Figure 7
Figure 7
Receiver operating characteristics from the Poly U multispectral palm print database: (a) left palm, (b) right palm, and (c) left and right palm.
Figure 8
Figure 8
Equal error rate ROC plot: (a) CASIA, (b) Poly U.
See this image and copyright information in PMC

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