Abstract
Biometric systems are used for identification- and verification-based applications such as e-commerce, physical access control, banking, and forensic. Among several kinds of biometric identifiers, finger knuckle print (FKP) is a promising biometric trait in the present scenario because of its textural features. In this paper, wavelet transform (WT) and Gabor filters are used to extract features for FKP. The WT approach decomposes the FKP feature into different frequency subbands, whereas Gabor filters are used to capture the orientation and frequency from the FKP. The information of horizontal subbands and content information of Gabor representations are both utilized to make the FKP template, and are stored for verification systems. The experimental results show that wavelet families along with Gabor filtering give a best FKP recognition rate of 96.60 %.
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References
Jain, A.K., Ross, A., Prabhakar, S.: An introduction to biometric recognition. IEEE Trans. Cir. syst. video technol. 14, 4–20 (2004).
Woodard, D.L., Flynn, P.J.: Finger surface as a biometric identifier. Comput. Vis. Image Underst. 100 (3), 357–384 (2005).
Kumar, A., Ravikanth, C.: Personal authentication using finger knuckle surface. IEEE Trans Inf. Forens. Secur. 4, 98–109 (2009).
Zhang, L., Zhang, L., Zhang, D., Zhu, H.: Online finger knuckle print verification for personal authentication. Pattern. Recognit. 43, 2560–2571 (2010).
Verma, G., Sinha, A.: Finger knuckle print verification using minimum average correlation energy filter. IJECS. 5, 233–246 (2014).
Mallat, S. : A theory of multiresolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Machine Intell. 11, 674–693 (1989).
Vetterli, M., Kovačević, J.: Wavelets and subband coding. Prentice Hall Englewood Cliffs New Jersey (1995).
Kim, J., Cho, S., Choi, J., Marks, R. J.: Iris Recognition using wavelet features. J. VLSI Signal Process. 38, 147–156 (2004).
Zhang, B. L., Zhang, H. H., Ge, S. S.: Face recognition by applying wavelet subband representation and kernel associative memory. IEEE Trans. Neural Networks.15, 166–177 (2004).
Kong, W. K., Zhang, D., Li, W.: Palmprint feature extraction using 2-D Gabor filters. Pattern Recognit. 36, 2339–2347 (2003).
Wang, R., Wang, G., Chen, Z., Zang, Z., Wang, Y.: palm vein identification system based on Gabor wavelet features. Neural Comput & Applic. 24, 161–168 (2013).
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Verma, G., Sinha, A. (2017). Finger Knuckle Print Recognition Based on Wavelet and Gabor Filtering. In: Raman, B., Kumar, S., Roy, P., Sen, D. (eds) Proceedings of International Conference on Computer Vision and Image Processing. Advances in Intelligent Systems and Computing, vol 459. Springer, Singapore. https://doi.org/10.1007/978-981-10-2104-6_4
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DOI: https://doi.org/10.1007/978-981-10-2104-6_4
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