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http://dx.doi.org/10.3807/JOSK.2015.19.5.467

Reflection-type Finger Vein Recognition for Mobile Applications  

Zhang, Congcong (Shandong Provincial Key Laboratory of Wireless Communication Technologies, School of Information Science and Engineering, Shandong University)
Liu, Zhi (Shandong Provincial Key Laboratory of Wireless Communication Technologies, School of Information Science and Engineering, Shandong University)
Liu, Yi (Shandong Provincial Key Laboratory of Wireless Communication Technologies, School of Information Science and Engineering, Shandong University)
Su, Fangqi (Shandong Provincial Key Laboratory of Wireless Communication Technologies, School of Information Science and Engineering, Shandong University)
Chang, Jun (Shandong Provincial Key Laboratory of Wireless Communication Technologies, School of Information Science and Engineering, Shandong University)
Zhou, Yiran (University of Electronic Science and Technology of China)
Zhao, Qijun (National Key Laboratory of Fundamental Science on Synthetic Vision, School of Computer Science, Sichuan University)
Publication Information
Journal of the Optical Society of Korea / v.19, no.5, 2015 , pp. 467-476 More about this Journal
Abstract
Finger vein recognition, which is a promising biometric method for identity authentication, has attracted significant attention. Considerable research focuses on transmission-type finger vein recognition, but this type of authentication is difficult to implement in mobile consumer devices. Therefore, reflection-type finger vein recognition should be developed. In the reflection-type vein recognition field, the majority of researchers concentrate on palm and palm dorsa patterns, and only a few pay attention to reflection-type finger vein recognition. Thus, this paper presents reflection-type finger vein recognition for biometric application that can be integrated into mobile consumer devices. A database is built to test the proposed algorithm. A novel method of region-of-interest localization for a finger vein image is introduced, and a scheme for effectively extracting finger vein features is proposed. Experiments demonstrate the feasibility of reflection-type finger vein recognition.
Keywords
Reflection-type finger vein recognition; Identity authentication; Biometrics; ROI localization;
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  • Reference
1 J. Yang and Y. Shi, "Finger-vein ROI localization and vein ridge enhancement," Pattern Recognit. Lett. 33, 1569-1579 (2012).   DOI   ScienceOn
2 T. Kato, M. Kondo, K. Hattori, R. Taguchi, M. Hoguro, and T. Umezaki, "Development of penetrate and reflection type finger vein certification," in Proc. IEEE International Symposium on MicroNano-Mechatronics and Human Science (Nagoya, Aichi, Japan, Nov. 2012), pp. 501-506.
3 J. Liu, J. Cui, D. Y. Xue, and X. Jia, "Palm-dorsa vein recognition based on independent principle component analysis," in Proc. IEEE International Conference on Image Analysis and Signal Processing (Central China Normal University, China, Oct. 2011), pp. 660-664.
4 J. C. Lee, "A novel biometric system based on palm vein image," Pattern Recognit. Lett. 33, 1520-1528 (2012).   DOI   ScienceOn
5 P. Promila and V. Laxmi, "Palmprint matching using LBP," in Proc. IEEE International Conference on Computing Sciences (Phagwara, India, Sept. 2012), pp. 110-115.
6 L. Mirmohamadsadeghi and A. Drygajlo, "Palm vein recognition with local binary patterns and local derivative patterns," in Proc. IEEE International Joint Conference on Biometrics (Washington D.C., USA, Oct. 2011), pp. 1-6.
7 K. S. Wu, J. C. Lee, T. M. Lo, K. C. Chang, and C. P. Chang, "A secure palm vein recognition system," J. Syst. Softw. 86, 2870-2876 (2013).   DOI   ScienceOn
8 M. R. Mu and Q. Q. Ruan, "Palmprint recognition based on statistical local binary orientation code," J. Electron. 8, 230-236 (2010).
9 C. Zhang, X. Li, Z. Liu, Q. Zhao, H. Xu, and F. Su, "The CFVD reflection-type finger vein image database with evaluation baseline," in Proc. Chinese Conference on Biometric Recognition (Shandong University, China, Nov. 2013), pp. 282-287.
10 N. Otsu, "A threshold selection method from gray-level histograms," Automatica 11, 23-27 (1975).
11 A. Rosenfeld and P. De La Torre, "Histogram concavity analysis as an aid in threshold selection," IEEE Trans. Syst. Man & Cybern. SMC-13, 231-235 (1983).   DOI   ScienceOn
12 M. I. Sezan, "A peak detection algorithm and its application to histogram-based image data reduction," Graph. Models Image Process 29, 47-59 (1985).   DOI
13 J. C. Olivo, "Automatic threshold selection using the wavelet transform," Graph. Models Image Process 56, 205-218 (1994).   DOI   ScienceOn
14 R. M. Haralock and L. G. Shapiro, Computer and Robot Vision (Addison-Wesley Longman Publishing Co., Inc., 1995), vol. I.
15 T. O. Binford, "Visual perception by computer," in Proc. IEEE Conference on Systems and Control (Miami, USA, Nov. 1971), p. 262.
16 D. Gabor, "Theory of communication. Part : The analysis of information," J. Inst. Electr. Eng.-Part III: Radio Commun. Eng. 93, 429-441 (1946).
17 J. Yang, Y. Shi, and J. Yang, "Finger-vein recognition based on a bank of Gabor filters," Lect. Notes Comput. Sci. 5994, 374-383 (2010).
18 T. Ojala, M. Pietikainen, and D. Harwood, "A comparative study of texture measures with classification based on featured distributions," Pattern Recognition 29, 51-59 (1996).   DOI   ScienceOn
19 Q. Li, Z. Qiu, and D. Sun, "Feature-level fusion of hand biometrics for personal verification based on Kernel PCA," in Proc. International Conference on Biometrics (Hong Kong, China, Jan. 2006), pp. 744-750.
20 E. C. Lee, H. C. Lee, and K. R. Park, "Finger vein recognition using minutia-based alignment and local binary pattern-based feature extraction," Int. J. Imaging Syst. & Technol. 19, 179-186 (2009).   DOI   ScienceOn
21 W. Jia, R. Hu, Y. Lei, Y. Zhao, and J. Gui, "Histogram of oriented lines for palmprint recognition," IEEE Transactions on Systems, Man, and Cybernetics: Systems 44, 385-395 (2014).   DOI
22 H. Zhang and D. Hu, "A palm vein recognition system," in Proc. IEEE International Conference on Intelligent Computation Technology and Automation (ICICTA) (Changsha, China, May 2010), vol. 1, pp. 285-288.
23 Y. B. Zhang, Q. Li, J. You, and P. Bhattacharya, "Palm vein extraction and matching for personal authentication," Lecture Notes in Computer Science 4781, 154-164 (2007).
24 G. K. O. Michael, T. Connie, L. S. Hoe, and A. T. B. Jin, "Design and implementation of a contactless palm vein recognition system," in Proc. The 2010 Symposium on Information and Communication Technology (Hanoi, Vietnam, Aug. 2010), pp. 92-99.
25 H. Ragheb and E. R. Hancock, "A probabilistic framework for specular shape-from-shading," Pattern Recognition 36, 407-427 (2003).   DOI   ScienceOn