Abstract
This paper presents a new finger-vein based method of personal identification. A reliable finger-vein region suitable for recognition is first acquired using our homemade imaging system. To exploit the finger-vein characteristics with high randomicity, a bank of Gabor filters specific to finger-vein analysis is then designed. Based on the spatial filtered images, finger-vein feature vectors are constructed for describing finger-vein characteristics in two filter scales. Finally, a fusion scheme in decision level is adopted to improve the reliability of identification. Experimental results are given to show the effectiveness of the proposed method in personal identification.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Xu, M., Sun, Q.: Vasculature Development in Embryos and Its Regulatory Mechanisms. Chinese Journal of Comparative Medicine 13(1), 45–49 (2003)
Zharov, V., Ferguson, S., Eidt, J., Howard, P., Fink, L., Waner, M.: Infrared Imaging of Subcutaneous Veins. Lasers in Surgery and Medicine 34(1), 56–61 (2004)
Kono, M., Memura, S.U., Miyatake, T., Harada, K., Ito, Y., Ueki, H.: Personal Identification System, USPatent No.6813010 Hitachi, United States (2004)
Miura, N., Nagasaka, A.: Feature Extraction of Finger-vein Pattern Based on Repeated Line Tracking and Its Application to Personal Identification. Machine Vision and Applications 15(4), 194–203 (2004)
Miura, N., Nagasaka, A., Miyatake, T.: Extraction of Finger-vein Patterns Using Maximum Curvature Points in Image Profiles. IEICE - Transactions on Information and Systems, 1185–1194 (2007)
Lian, Z., Rui, Z., Yu, C.: Study on the Identity Authentication System on Finger Vein. In: International Conference on Bioinformatics and Biomedical Engineering, pp. 1905–1907 (2008)
Zhang, Z., Ma, S., Han, X.: Multiscale Feature Extraction of Finger-Vein Patterns Based on Curvelets and Local Interconnection Structure Neural Network. In: International Conference on Pattern Recognition, pp. 145–148 (2006)
Vlachos, M., Dermatas, E.: Vein Segmentation in Infrared Images Using Compound Enhancing and Crisp Clustering. In: Gasteratos, A., Vincze, M., Tsotsos, J.K. (eds.) ICVS 2008. LNCS, vol. 5008, pp. 393–402. Springer, Heidelberg (2008)
Jie, Z., Ji, Q., Nagy, G.: A Comparative Study of Local Matching Approach for Face Recognition. IEEE Transactions on Image Processing 16(10), 2617–2628 (2007)
Ma, L., Tan, T., Wang, Y., Zhang, D.: Personal Identification Based on Iris Texture Analysis. IEEE Trans. on Pattern Analysis and Machine Intelligence 25(12), 1519–1533 (2003)
Jain, A.K., Chen, Y., Demirkus, M.: Pores and Ridges: High-Resolution Fingerprint Matching Using Level 3 Features. IEEE Trans. on Pattern Analysis and Machine Intelligence 29(1), 15–27 (2007)
Laadjel, M., Bouridane, A., Kurugollu, F., Boussakta, S.: Palmprint Recognition Using Fisher-Gabor Feature Extraction. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 1709–1712 (2008)
Shi, Y., Yang, J., Wu, R.: Reducing Illumination Based on Nonlinear Gamma Correction. In: IEEE international Conference on Image Processing, pp. 529–532 (2007)
Daugman, J.G.: Uncertainty Relation for Resolution in Space, Spatial Frequency, and Orientation Optimized by 2D Visual Cortical Filters. Journal of the Optical Society of America 2(7), 1160–1169 (1985)
Ren, X., Yang, J., Li, H., Wu, R.: Multi-fingerprint Information Fusion for Personal Identification Based on Improved Dempster-Shafer Evidence Theory. In: IEEE International Conference on Electronic Computer Technology, pp. 281–285 (2009)
Yager, R.: On the D-S Framework and New Combination Rules. Information Sciences 41(2), 93–138 (1987)
Brunelli, R., Falavigna, D.: Person Identification Using Multiple Cues. IEEE Trans. on Pattern Analysis and Machine Intelligence 17(10), 955–966 (1995)
Phillips, J., Moon, H., Rizvi, S., Rause, P.: The FERET Evaluation Methodology for Face Recognition Algorithms. IEEE Trans. on Pattern Analysis and Machine Intelligence 22(10), 1090–1104 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yang, J., Shi, Y., Yang, J. (2010). Finger-Vein Recognition Based on a Bank of Gabor Filters. In: Zha, H., Taniguchi, Ri., Maybank, S. (eds) Computer Vision – ACCV 2009. ACCV 2009. Lecture Notes in Computer Science, vol 5994. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12307-8_35
Download citation
DOI: https://doi.org/10.1007/978-3-642-12307-8_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12306-1
Online ISBN: 978-3-642-12307-8
eBook Packages: Computer ScienceComputer Science (R0)