Local Descriptor Approach to Wrist Vein Recognition with DVH-LBP Domain Feature Selection Scheme

Dini Fronitasari, - Basari, Dadang Gunawan


Local Binary Pattern (LBP) is one of the well-known image recognition descriptors for texture-based images due to its superiority. LBP can represent texture well due to its ability to discriminate and compute efficiency. However, when it is used to describe textures that are barely visible, such as vein images (especially contactless vein), its discrimination ability is reduced, which leads to lower performance. LBP has extensively been implemented for features extraction in recognition system of hand, eye, face, eye, and other images. Nowadays, there are a lot of developments of hand recognition systems as a hand is a part of the body that can be easily used in the recognition process and it is easier to contact the sensor when taking the image (user-friendly). In particular, a hand consists of various parts that can be used, such as palm and fingers. Other parts like dorsal and wrist can also be used as they have unique characteristics, i.e., they are different from each other, and they do not change with ages. Changes in pixel intensity can be derived from skeletal vein images to distinguish individuals in palm vein recognition. In the previous paper, we proposed a method diagonal, vertical, horizontal local binary pattern (DVH-LBP) for implementing the palm vein recognition system successfully. Through this work, we improve our previous procedure and implement the improved method for recognizing wrist. In particular, this study proposes a new and robust directional extraction technique for encoding the functions of the wrist vein in a simple representation of binary numbers. Simulation results show the low equal error rate (ERR) of the proposed technique is 0.012, and the recognition rate is 99.4%.


wrist vein; pattern recognition; feature extraction; diagonal; vertical; horizontal local binary pattern (DVH-LBP).

Full Text:



T.Ojala, M.Pietikainen, and T.Maenpaa, “Multiresolution gray-scale and rotation invariant texture classiï¬cation with local binary patterns,†IEEE Trans.Patt. Anal.Mach.Intell., vol.24, no.7, pp.971–987, 200

D.Fronitasari and D. Gunawan, " Palm Vein recognition by Using of Modified Local Binary Pattern for Extraction Feature, 2017 in 15th International Conference on Quality in Research (QIR) 2017, 2017

Y. Zhou and A. Kumar, “Human identiï¬cation using palm-vein images,†IEEE Trans. Inf. Forensics Security, vol. 6, no. 4, pp. 1259–1274, Dec. 2011.

L. Mirmohamadsadeghi and A. Drygajlo, “Palm vein recognition with local binary patterns and local derivative patterns,†in Proc. Int. Joint Conf. Biometrics, Oct. 2011, pp. 1–6.

M. Fischer, M. Rybnicek, and S. Tjoa, “A novel palm vein recognition approach based on enhanced local Gabor binary patterns histogram sequence,†in Proc. 19th Int. Conf. Syst., Signals, Image Process., Apr. 2012, pp. 429–432.

J.-C. Lee, “A novel biometric system based on palm vein image,†Pattern Recognit. Lett., vol. 33, no. 12, pp. 1520–1528, Sep. 2012. [9] Y. Zhou and A. Kumar, “Contactless palm vein identiï¬cation using multiple representations,†in Proc. 4th IEEE Int. Conf. Biometrics, Theory Appl. Syst., Sep. 2010, pp. 1–6.

Wenxiong Kang, “Contactless palm vein recognition using a mutual foreground-based local binary pattern, EEE Transactions on Information Forensics and Security ( Volume: 9, Issue: 11, Nov. 2014).

A. Kong, D. Zhang, and M. Kamel, “A survey of palmprint recognition,†Pattern Recognit., vol. 42, no. 7, pp. 1408–1418, 2009

C.-L. Lin and K.-C. Fan, "Biometric veriï¬cation using thermal images of palm-dorsal vein patterns," IEEE Trans. Circuits Syst. Video Technol., vol. 14, no. 2, pp. 199–213, Feb. 2004.

Y. Wang, K. Li, and J. Cui, "Hand-dorsal vein recognition based on partition local binary pattern," in Proc. IEEE 10th Int. Conf. Signal Process., Oct. 2010, pp. 1671–1674.

C.-B. Hsu, S.-S. Hao, and J.-C. Lee, “Personal authentication through dorsal hand vein patterns,†Opt. Eng., vol. 50, no. 8, p. 087201, Jul. 2011.

W. Yang, X. Huang, F. Zhou, and Q. Liao, “Comparative competitive coding for personal identiï¬cation by using ï¬nger vein and ï¬nger dorsal texture fusion,†Inf. Sci., vol. 268, pp. 20–32, Jun. 2014.

W. Kang, “Vein pattern extraction based on vectorgrams of maximal intra-neighbor difference,†Pattern Recognit. Lett., vol. 33, no. 14, pp. 1916–1923, 2012.

H. 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. Imag. Syst. Technol., vol. 19, no. 3, pp. 179–186, 2009.

H. C. Lee, B. J. Kang, E. C. Lee, and K. R. Park, “Finger vein recognition using weighted local binary pattern code based on a support vector machine,†J. Zhejiang Univ. Sci. C, vol. 11, no. 7, pp. 514–524, 2010.

W. Song, T. Kim, H. C. Kim, J. H. Choi, H.-J. Kong, and S. R. Lee, “A ï¬nger-vein veriï¬cation system using mean curvature,†Pattern Recognit. Lett., vol. 32, no. 11, pp. 1541–1547, Aug. 2011.

B. A. Rosdi, C. W. Shing, and S. A. Suandi, “Finger vein recognition using local line binary pattern,†Sensors, vol. 11, no. 12, pp. 11357–11371, 2011.

J. Yang and Y. Shi, “Towards ï¬nger-vein image restoration and enhancement for ï¬nger-vein recognition,†Inf. Sci., vol. 268, pp. 33–52, Jun. 2014.

J. E. Suarez Pascual, J. Uriarte-Antonio, R. Sanchez-Reillo, and M. G. Lorenz, “Capturing hand or wrist vein images for biometric authentication using low-cost devices,†in Proc. 6th Int. Conf. Intell. Inf. Hiding Multimedia Signal Process., Oct. 2010, pp. 318–322

H. Zhang, C. Tang, A. W.-K. Kong, and N. Craft, “Matching vein patterns from color images for forensic investigation,†in Proc. IEEE 5th Int. Conf. Biometrics, Theory, Appl., Syst., Sep. 2012, pp. 77–84.

Y. Lin, E. Y. Du, Z. Zhou, and N. L. Thomas, “An efï¬cient parallel approach for Sclera vein recognition,†IEEE Trans. Inf. Forensics Security, vol. 9, no. 2, pp. 147–157, Feb. 2014.

Z. Zhou, E. Y. Du, N. L. Thomas, and E. J. Delp, "A new human identiï¬cation method: Sclera recognition," IEEE Trans. Syst., Man, Cybern. A Syst., Humans, vol. 42, no. 3, pp. 571–583, May 2012.

Abhijit Das, Umapada Pal, and Miguel errer and M.Bluemestein, " A new Wirst Vein Biometric System" ‘In Proc. 13th international conference on Intelligent Systems Design and Applications, 74-79, 2014.

Wang, J.-G., Yau, W.-Y., Suwandy, A., et al.: ‘Fusion of palmprint and palm vein images for person recognition based on ‘Laplacianpalm’ feature’. 2007 IEEE Conf. on Computer Vision and Pattern Recognition, Minneapolis, USA, June 2007, pp. 1–8

Wang, L., Leedham, G., Siu-Yeung Cho, D.: ‘Minutiae feature analysis for infrared hand vein pattern biometrics,’ Pattern Recogn., 2008, 41, (3), pp. 920–929

Zhou, Y., Kumar, A.: ‘Human identification using palm-vein images,’ IEEE Trans. Inf. Forensics Sec., 2011, 6, (4), pp. 1259–1274

Han, W.Y., Lee, J.C.: ‘Palm vein recognition using adaptive Gabor filter,’ Expert Syst. Appl., 2012, 39, (18), pp. 13225–13234

Wang, J.-G., Yau, W.-Y., Suwandy, A., et al.: ‘Person recognition by fusing palmprint and palm vein images based on ‘Laplacianpalm’ representation,’ Pattern Recogn., 2008, 41, (5), pp. 1514–1527

Kang, W.X., Liu, Y., Wu, Q.X., et al.: ‘Contact-free palm-vein recognition based on local invariant features,’ PLoS One, 2014, 9, (5), pp. 1239–1245

Kang, W.X., Wu, Q.X.: ‘Contactless palm vein recognition using a mutual foreground-based local binary pattern,’ IEEE Trans. Inf. Forensics Sec., 2014, 9, (11), pp. 1974–1985

Yan, X.K., Kang, W.X., Deng, F.Q., et al.: ‘Palm vein recognition based on multi-sampling and feature-level fusion,’ Neurocomputing, 2015, 151, (151), pp. 798–807

Xin Ma, Xiaojun Jing .,Yuanhao Cui.,Junsheng Mui. : ‘ Palm Vein Recognition scheme based on adaptive Gabor Filter’, IET journal

R. Kabaciński and M. Kowalski. Human vein pattern segmentation from low-quality images comparison of methods. Image Processing and Communications Challenges 2, 84 of Advances in Intelligent and Soft Computing, 105-112, 2010.

R. Kabaciński and M. Kowalski. Human vein pattern correlation- a comparison of segmentation methods. Computer Recognition Systems 4, 95 of Advances in Intelligent and Soft Computing, 51-59, 2011.

R. Kabaciński and M. Kowalski.Vein pattern database and benchmark results. Electronics Letters, 47(20), 1127-1128, 2011.

D. Hartung, M. A. Olsen, H. Xu, and C. Busch, Spectral minutiae for vein pattern recognition. In Biometrics (IJCB), 2011 International Joint Conference on, 1 –7, 2011.

D. Hartung, M. A. Olsen, H. Xu, H. T. Nguyen, and C. Busch, Comprehensive analysis of spectral minutiae for vein pattern recognition. In IET Biometrics (1), 25–36, 2012.

J. Uriarte-Antonio, D. Hartung, J. Pascual, and R. Sanchez-Reillo, Vascular biometrics based on a minutiae extraction approach. In Security Technology(ICCST), IEEE International Carnahan Conference on , 1 –7,2011.

M. Akhloufi . and A. Bendada, Capturing Hand or Wrist Vein Images for Biometric Authentication Using Low-Cost Devices, Sixth International Conference on Intelligent InformationHiding and Multimedia Signal Processing (IIH-MSP), 318 – 322, 2010

J. E. Suarez Pascual, ,J. Uriarte-Antonio, R. Sanchez-Reillo,M.G. Lorenz, . Hand and Wrist Physiological FeaturesExtraction for near Infrared Biometrics, Canadian Conference on Computer and Robot Vision, CRV '08, 341.

PUT vein dataset available at http://biometrics.put.poznan.pl/home/new-news-page/

A. Jain, R.M. Bolle, S. Pankati, Biometrics: Personal Identification in a Networked Society(Kluwer Academic Publishers, Dordrecht, 1999)

J.L. Wayman, Technical testing and evaluation of biometric identification devices, in Biometrics: Personal Identification in Networked Society (Kluwer Academic, Dordrecht, 1998).

DOI: http://dx.doi.org/10.18517/ijaseit.9.3.8032


  • There are currently no refbacks.

Published by INSIGHT - Indonesian Society for Knowledge and Human Development