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http://dx.doi.org/10.5909/JBE.2018.23.3.395

User Identification Method using Palm Creases and Veins based on Deep Learning  

Kim, Seulbeen (Department of Electrical and Electronic Engineering, Konkuk University)
Kim, Wonjun (Department of Electrical and Electronic Engineering, Konkuk University)
Publication Information
Journal of Broadcast Engineering / v.23, no.3, 2018 , pp. 395-402 More about this Journal
Abstract
Human palms contain discriminative features for proving the identity of each person. In this paper, we present a novel method for user verification based on palmprints and palm veins. Specifically, the region of interest (ROI) is first determined to be forced to include the maximum amount of information with respect to underlying structures of a given palm image. The extracted ROI is subsequently enhanced by directional patterns and statistical characteristics of intensities. For multispectral palm images, each of convolutional neural networks (CNNs) is independently trained. In a spirit of ensemble, we finally combine network outputs to compute the probability of a given ROI image for determining the identity. Based on various experiments, we confirm that the proposed ensemble method is effective for user verification with palmprints and palm veins.
Keywords
user verification; palmprints and palm veins; multispectral palm images; ensemble of CNNs;
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1 L.Wang, G. Leedham, and D. S. Y. Cho, “Minutiae feature analysis for infrared hand vein pattern biometrics,” Pattern Recognition, Vol. 41, No. 3, pp. 920-929, Mar. 2008.   DOI
2 J. Wang, W. Yau, A. Suwandy, and E. Sung, "Fusion of palmprint and palm vein images for person recognition based on "Laplacianpalm" feature," Computer Vision and Pattern Recognition Workshop on Biometrics, Minneapolis, USA, pp. 1-8, Jun. 2007.
3 Y. Zhou, and A. Kumar, "Contactless palm vein identification using multiple representations," 4th IEEE Int. Conf. Biometrics,Theory Appl. Syst., Washington DC, USA, pp. 1-6, Sep. 2010.
4 L. Mirmohamadsadeghi, and A. Drygajlo, "Palm vein recognition with local binary patterns and local derivative patterns," Int. Joint Conf. Biometrics, Washingtond DC, USA, pp. 1-6, Oct. 2011.
5 W. Kang, W. Liu and X. Yue, “Contact-free palm-vein recognition based on local invariant features,” PloS one, Vol. 9, No. 5, pp. 1239-1245, May 2014.
6 Y. Hao, Z. Sun, T. Tan, and C. Ren, "Multispectral palm image fusion for accurate contact-free palmprint recognition," 15th IEEE Int. Conf. Image Process., San Diego, USA, pp. 281-284, Oct. 2008.
7 L. Mirmohamadsadeghi, and A. Drygajlo, “Palm vein recognition with local texture patterns,” IET Biometrics, Vol. 3, No. 4, pp. 198-206, Jan. 2014.   DOI
8 P. Wang, and D. Sun, "A research on palm vein recognition," IEEE 13th International conference on Signal Processing (ICSP), pp. 1347-1351, Nov. 2016.
9 W. Han, and J. Lee, "Palm vein recognition using adaptive Gabor filter," Expert Systems with Applications, Vol. 39, No.18, pp. 13225-13234, Dec. 2012.   DOI
10 X. Ma, X. Jing, Y. Cui, and J. Mu, “Palm vein recognition scheme based on an adaptive Gabor filter,” IET Biometrics, Vol. 6, No. 5, pp. 325-333, Dec. 2016.
11 N. Otsu, “A threshold selection method from gray-level histograms,” IEEE Transactions on Systems, Man, and Cybernetics, Vol. 9, No. 1, pp. 62-66, Jan. 1979.   DOI
12 Y. Zhou, and A. Kumar, "Human identification using palm-vein images," IEEE Trans. Inf. Forensics Security, Vol. 6, No.4, pp. 1259-1274, Dec. 2011.   DOI
13 A. Reza, “Realization of the contrast limited adaptive histogram equalization (CLAHE) for real-time image enhancement,” Journal of VLSI Signal Processing Systems for Signal, Image and Video technology, Vol. 38, No. 1, pp. 35-44, Aug. 2004.   DOI
14 R.C. Gonzalez, and R.E. Woods, Digital Image Processing, second ed., Upper Saddle River, N.J.: Prentice Hall, pp. 187-191, 2002.
15 A. Krizhevsky, I. Sutskever and G. E. Hinton. "Imagenet classification with deep convolutional neural networks," Neural Inf. Process. Syst., Lake Tahoe, USA, pp. 1097-1105, Dec. 2012.
16 K. Simonyan, A. Aisserman, "Very deep convolutional networks for large-scale image recognition," International Conference on Learning Representations, San Diego, USA, May 2015.
17 CASIA-MS-Palmprint, http://biometrics.ideal-test.org/
18 W. Kang, and Q. Wu, "Contactless palm vein recognition using a mutual foreground-based local binary pattern," IEEE Transactions on Information Forensics and Security, Vol. 9, No. 11, pp. 1974-1985, Nov. 2014.   DOI