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Polish Information Processing Society
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Annals of Computer Science and Information Systems, Volume 13

Communication Papers of the 2017 Federated Conference on Computer Science and Information Systems

Multimodal Biometric System for Identity Verification Based on Hand Geometry and Hand Palm's Veins

DOI: http://dx.doi.org/10.15439/2017F14

Citation: Communication Papers of the 2017 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 13, pages 207212 ()

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Abstract. This project was developed aiming at the implementation of a multibiometric systemcapable to handle hand palm images acquired using a touchless approach. Thisconsiderable increases the difficult of the image processing task due to the fact thatthe images from the same person may vary significantly depeding on the relative position of the hand regarding the sensor. A modular sofware tool was developed, providing the user a method for each of these steps: initialimage preparation, the feature extraction, processing and fusion, ending withthe classification, thus making the researcher'stask a lot easier and faster. The biometric features used for identification includehand geometry features as well palm vein textures. For the hand geometry data, analgorithm for determining finger tips and hand valleys was proposed and from there was possibleto extract a handful of other features related to the geometry of the hand. The handpalm veins' texture features were extracted from a rectangle generated based on thehand's center of mass. The texture descriptor chosen was the Histogram ofGradients. In possession with all the biometric data, the fusion was done on featurelevel. Support Vector Machine technique was used for the classification. Thedatabase chosen for the development of this project was the CASIA Multi- SpectralPalmprint Image Database V1.0. The images used corresponds to the 940nmspectrum due to allowing the visualization of the hand palm's veins. The achievedresult for the hand geometry was an EER of 4,77\%, for the palm veins an EER of3,11\% and changing the threshold value a FAR of 0,50\% and a FRR of 4,82\% wereachieved. For the fusion of both biometrics systems the final result was an EER of 2,33\% witha FAR of 1,30\% and a FRR of 4,27\%.


  1. Jain, et. al.. Handbook of Biometrics. 1st Edition. Springer, New York, 2008.
  2. Casia-MS-Palmprint V1. Availableat: http://biometrics.idealtest.org.
  3. Gonzalez , R. C.; Woods, R. E. Digital Image Processing, 3rd edition, Prentice-Hall, 2008.
  4. OTSU N. A threshold selection method from gray-level histograms. IEEE Trans. Sys., Man., Cyber, 1979.
  5. Canny, John. “A Computational Approach to Edge Detection”, IEEE Transaction onPattern Analysis and Machine Intelligence, Vol. 8, pp. 679-98, 1986.
  6. AForge.NET framework V2.2.5. Available at: http://www.aforgenet.com.
  7. Kekre H, Sarode T, Vig R. An effectual method for extraction of ROI of palmprints. In: Communication, Information Computing Technology (ICCICT), International Conference on . 2012. P. 1-5.
  8. Kalluri, H. K.; Prasad, M. V. N. K.; AGARWAL, A. 2012. Dynamic ROI extraction algorithm for palmprints. In Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part II (ICSI'12), Ying Tan, Yuhui Shi, and Zhen Ji (Eds.), Vol. Part II. Springer-Verlag, Berlin, Heidelberg, 217-227.
  9. Triggs B, Dalal N. Histogram of Oriented Gradients for Human Detection. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005.
  10. Aksoy, S.; Haralick, R. "Feature normalization and likelihood-based similarity measures for image retrieval," Pattern Recognit. Lett., Special Issue on Image and Video Retrieval, 2000.
  11. Chang, C.-C.; Lin, C.-J. Libsvm: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, v. 2, p. 27:1–27:27, 2011. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
  12. Xin, Cui; Wu, Xiangqian; Qiushi, Zhao; Youbao, Tang. “A Contactless HandShape Identification System”, 3rd International Conference on Advanced Computer Control, pp. 561-565, 2011
  13. Guo, Jing-Ming; Hsia, Chih-Hsein; Liu, Yun-Fu; Yu, Jie-Cyun; Chu, Mei-Hui; Le, Thanh-Nam. “Contact-free hand geometry-based identification system”, Expert System with Applications, Vol. 39, No. 14, pp. 11728-11736, 2012.
  14. Lee, Jen-Chun. “A novel biometric system based on palm vein image”, Pattern Recognition Letters, Vol. 33, No. 12, pp. 1520-1528, 2012.
  15. Gangopadhyay, Ahana; Chatterjee, Oindrila; Chatterjee, Amitava. “Hand shape based biometric authentication system using radon transform and collaborative representation based classification”, IEEE Second International Conference on Image Information Processing, pp. 635-639, 2013.
  16. Singh, Aditya P.; Thakur, Ranjan K.; Kumar, Arabind; Baksh, Ram. “User Authentication Using Hand Images”, International Journal of Science and Research, Vol. 3, No. 3, pp. 317-322, 2014.
  17. Abbas, Asmaa M. J.; George, Loay E. “Palm Vein Recognition and Verification System Using Local Average of Vein Direction”, International Journal of Scientific & Engineering Research, Vol. 5, No. 4, pp. 1026-1033, 2014.
  18. Wang, Ran; Wang, Guoyou; Chen, Zhong; Zeng, Zhigang; Wang, Yong. “A palm vein identification system based on Gabor wavelet features”, Neural Computing and Applications, Vol. 24, pp. 161-168, 2014.
  19. Elnasir, Selma; Shamsuddin, Siti Mariyam.” Palm Vein Recognition based on 2D- Discrete Wavelet Transform and Linear Discrimination Analysis”, International Journal of Advances in Soft Computing and its Application, Vol. 6, No. 3, pp. 43-59,2014.
  20. Kang, Wenxiong; LIU, Yang; WU, Qiuxia; YUE, Xishun. “Contact-Free Palm-Vein Recognition Based on Local Invariant Features”, PLoS ONE, Vol. 9, No. 5, pp. 1- 12, 2014.
  21. Yan, Xuekui; Kang, Wenxiong; Deng, Feiqi; Wu, Qiuxia. “Palm vein recognition based on multi-sampling and feature-level fusion”, Neurocomputing, Vol. 151, pp. 798-807, 2015.
  22. Said, Y; Atri M.; Tourki, R. Human Detection Based on Integral Histograms of Oriented Gradients and SVM.Conference: International Conference on Communications, Computing and Control Applications, 2011.
  23. Candès, E.; Donoho, D. "Curvelets – a surprisingly effective nonadaptive representation for objects with edges." In: A. Cohen, C. Rabut and L. Schumaker, Editors, Curves and Surface Fitting: Saint-Malo 1999, Vanderbilt University Press, Nashville (2000), pp. 105–120.
  24. Rafiee, J. et al. Feature extraction of forearm EMG signals for prosthetics, Expert Systems with Applications 38, 2011.
  25. Park, G.; Kim, S.; Hand Biometric Recognition Based on Fused Hand Geometry and Vascular Patterns. Sensors, vol. 13, n. 3, pp. 2895 – 2910, 2013
  26. Park, G.; Kim, S.; Hand Biometric Recognition Based on Fused Hand Geometry and Vascular Patterns. Sensors, vol. 13, n. 3, pp. 2895 – 2910, 2013.
  27. BiometricsLab v2 Available at: https://github.com/dudu84/BiometricsLab-v2