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. 2014 Feb 17:2014:246083.
doi: 10.1155/2014/246083. eCollection 2014.

Palm vein verification using multiple features and locality preserving projections

Ali Mohsin Al-Juboori  1 , Wei Bu  2 , Xiangqian Wu  3 , Qiushi Zhao  3
Affiliations

Palm vein verification using multiple features and locality preserving projections

Ali Mohsin Al-Juboori et al. ScientificWorldJournal. .

Abstract

Biometrics is defined as identifying people by their physiological characteristic, such as iris pattern, fingerprint, and face, or by some aspects of their behavior, such as voice, signature, and gesture. Considerable attention has been drawn on these issues during the last several decades. And many biometric systems for commercial applications have been successfully developed. Recently, the vein pattern biometric becomes increasingly attractive for its uniqueness, stability, and noninvasiveness. A vein pattern is the physical distribution structure of the blood vessels underneath a person's skin. The palm vein pattern is very ganglion and it shows a huge number of vessels. The attitude of the palm vein vessels stays in the same location for the whole life and its pattern is definitely unique. In our work, the matching filter method is proposed for the palm vein image enhancement. New palm vein features extraction methods, global feature extracted based on wavelet coefficients and locality preserving projections (WLPP), and local feature based on local binary pattern variance and locality preserving projections (LBPV_LPP) have been proposed. Finally, the nearest neighbour matching method has been proposed that verified the test palm vein images. The experimental result shows that the EER to the proposed method is 0.1378%.

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Figures

Figure 1
Figure 1
System flowchart.
Figure 2
Figure 2
Palm vein image enhancements.
Figure 3
Figure 3
Decomposition algorithm of 2D wavelet transform [12].
Figure 4
Figure 4
db2 result.
Figure 5
Figure 5
LBP neighbours sets for different (P, R) [5, 13].
Figure 6
Figure 6
Palm vein image and LBPV histogram.
Figure 7
Figure 7
Flowchart of the feature vectors extraction.
Figure 8
Figure 8
(a) Distribution of the genuine user and impostor without enhancement using cosine classifier. (b) ROC curve without enhancement to different classifiers.
Figure 9
Figure 9
(a) Distribution of the genuine user and impostor without LPP using Euclidian classifier. (b) ROC curve without LPP to different classifiers.
Figure 10
Figure 10
(a) Distribution of the genuine user and impostor based WLPP using Euclidian classifier. (b) ROC curve based WLPP to different classifiers.
Figure 11
Figure 11
(a) Distribution of the genuine user and impostor based LBPV_LPP using Manhattan classifier. (b) ROC curve based LBPV_LPP to different classifiers.
Figure 12
Figure 12
(a) Distribution of the genuine user and impostor to the proposed system using Euclidian classifier. (b) ROC curve to different classifiers.
See this image and copyright information in PMC

References

    1. Annemarie Nadort. The Hand Vein Pattern Used as a Biometric Feature. Amsterdam, The Netherlands: Master Literature Thesis; 2007.
    1. Lee HC, Kang BJ, Lee EC, Park KR. Finger vein recognition using weighted local binary pattern code based on a support vector machine. Journal of Zhejiang University C. 2010;11(7):514–524.
    1. Zhang D, Guo Z, Lu G, Zhang L, Liu Y, Zuo W. Online joint palmprint and palmvein verification. Expert Systems with Applications. 2011;38(3):2621–2631.
    1. Wang Y-D, Yan Q-Y, Li K-F. Hand vein recognition based on multi-scale LBP and wavelet. Proceedings of the International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR ’11); July 2011; Guilin, China. pp. 214–218.
    1. Wang K-Q, Krisa AS, Wu X-Q, Zhao Q-S. Finger vein recognition using LBP variance with global matching. Proceedings of the International Conference on Wavelet Analysis and Pattern Recognition; 2012; pp. 196–200.

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