Development of an Embedded Palm Vein Imaging Prototype

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    This paper shares one of the available options in developing an embedded palm vein imaging prototype. The prototype was developed by the Raspberry Pi SBC to promote portability of the embedded system. With an integrated illumination circuit utilizing two near infrared (NIR) peak wavelengths of 850 nm and 870 nm, this paper explores the ability of the prototype to capture palm vein pattern information. The prototype program, and image analysis were executed by Python language environment and OpenCV module binding. The captured palm images were compared with palm image datasets from the Chinese Academy of Sciences’ Institute of Automation (CASIA) and the Hong Kong Polytechnic University (PolyU). The comparison was done in terms of observation of the image recorded and palm vein pattern revealed, and also through image assessment metrics. Results show that palm images captured by the prototype has the ability to record vein pattern information in the image with pixel-by-pixel similarity rate of 96.54% (median) for the extracted vein pattern, compared to the CASIA (median: 96.07%) and PolyU (median: 90.99%) datasets. As such, the developed prototype can be enhanced its usage not only for biometric acquisition, but also for medical purpose.

     

     


  • Keywords


    Embedded System; Palm Vein Biometric; Prototype Development; Single Board Computer.

  • References


      [1] J. A. Unar, W. C. Seng, and A. Abbasi, “A Review of Biometric Technology Along with Trends and Prospects,” Pattern Recognit., Feb. 2014.

      [2] W. Kang, Y. Liu, Q. Wu, and X. Yue, “Contact-Free Palm-Vein Recognition Based on Local Invariant Features,” PLoS One, vol. 9, no. 5, pp. 1–12, Jan. 2014.

      [3] D. Fronitasari and D. Gunawan, “Palm vein recognition by using modified of local binary pattern (LBP) for extraction feature,” in 2017 15th International Conference on Quality in Research (QiR) : International Symposium on Electrical and Computer Engineering, 2017, vol. 2017–Decem, pp. 18–22.

      [4] A. M. Al-Juboori, W. Bu, X. Wu, and Q. Zhao, “Palm Vein Verification Using Multiple Features and Locality Preserving Projections,” Sci. World J., vol. 2014, Jan. 2014.

      [5] Y. Liu, Y. Zhou, S. Qiu, J. Qin, and Y. Nie, “Real-time locating method for palmvein image acquisition,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2015, vol. 9219, pp. 94–110.

      [6] J.-W. Wang and T.-H. Chen, “Building Palm Vein Capturing System for Extraction,” in 2011 21st International Conference on Systems Engineering, 2011, pp. 311–314.

      [7] R. Fuksis, M. Greitans, O. Nikisins, and M. Pudzs, “Infrared Imaging System for Analysis of Blood Vessel Structure,” Electron. Electr. Eng., vol. 1, no. 97, pp. 45–48, 2010.

      [8] A. N. Che Rosli, “Embedded System for Biometric Identification,” Robot Vision, InTech, pp. 557–583, 2010.

      [9] G. K. O. Michael, T. Connie, and A. B. J. Teoh, “A Contactless Biometric System Using Multiple Hand Features,” J. Vis. Commun. Image Represent., vol. 23, no. 7, pp. 1068–1084, Oct. 2012.

      [10] L. Wang and G. Leedham, “Near- and Far- Infrared Imaging for Vein Pattern Biometrics,” in 2006 IEEE International Conference on Video and Signal Based Surveillance, 2006.

      [11] M. Watanabe, T. Endoh, M. Shiohara, and S. Sasaki, “Palm Vein Authentication Technology and Its Applications,” in Proceeedings of the Biometric Consortium Conference, 2005, pp. 19–21.

      [12] V. Paquit, J. R. Price, F. Mériaudeau, K. W. Tobin, and T. L. Ferrell, “Combining Near-infrared Illuminants to Optimize Venous Imaging,” in Medical Imaging 2007: Visualization and Image-Guided Procedures, 2007.

      [13] J. E. S. 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 2010 Sixth International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP), 2010, pp. 318–322.

      [14] K. I. Ahmed, M. H. Habaebi, and M. R. Islam, “A real time vein detection system,” Indones. J. Electr. Eng. Comput. Sci., vol. 10, no. 1, pp. 129–137, 2018.

      [15] S. Juric and B. Zalik, “An Innovative Approach to Near-Infrared Spectroscopy Using a Standard Mobile Device and Its Clinical Application in The Real-Time Visualization of Peripheral Veins,” BMC Med. Inform. Decis. Mak., vol. 14, no. 100, pp. 1–9, 2014.

      [16] K. K. Nundy and S. Sanyal, “A Low Cost Vein Detection System Using Integrable Mobile Camera Devices,” in 2010 Annual IEEE India Conference (INDICON), 2010, pp. 3–5.

      [17] OpenCV Development Team, “OpenCV Documentation,” 2015. [Online]. Available: http://docs.opencv.org/.

      [18] B. Horan, Practical Raspberry Pi. Berkeley, CA: Apress, 2013.

      [19] Rolando Murillo, “CherryPy A Minimalist Python Web Framework,” 2018. [Online]. Available: https://cherrypy.org/#Features. [Accessed: 5-Jul-2018].

      [20] Z. M. Noh, A. R. Ramli, M. Hanafi, and M. I. Saripan, “Acquiring Palm Vein Patterns for Visual Interpretation,” in 2015 2nd International Conference on Biomedical Engineering (ICoBE), 2015, no. March, pp. 1–5.

      [21] Raspberry Pi Foundation, “Raspberry Pi NoIR Camera - Infrared Camera,” 2015. [Online]. Available: https://www.raspberrypi.org/products/pi-noir-camera/. [Accessed: 17-Nov-2015].

      [22] N. S. Prakash and N. Venkatram, “Establishing Efficient Security Scheme in Home IOT Devices through Biometric Finger Print Technique,” Indian J. Sci. Technol., vol. 9, no. 17, pp. 1–8, 2016.

      [23] CASIA, “CASIA-MS-PalmprintV1,” 2014. [Online]. Available: http://biometrics.idealtest.org.

      [24] PolyU, “PolyU Multispectral Palmprint Database,” 2014. [Online]. Available: http://www.comp.polyu.edu.hk/~biometrics/MultispectralPalmprint/MSP.htm.

      [25] D. Zhang, Z. Guo, G. Lu, L. Zhang, and W. Zuo, “An Online System of Multispectral Palmprint Verification,” IEEE Trans. Instrum. Meas., vol. 59, no. 2, pp. 480–490, Feb. 2010.

      [26] Z. Guo, D. Zhang, L. Zhang, W. Zuo, and G. Lu, “Empirical study of light source selection for palmprint recognition,” Pattern Recognit. Lett., vol. 32, no. 2, pp. 120–126, 2011.

      [27] Z. M. Noh, “Framework of Operations for Palm Vein Pattern Extraction using Image Processing,” in Advances in Computer Vision Applications and Techniques, A. R. Syafeeza, Ed. Penerbit Universiti, UTeM, 2018, pp. 79–96.

      [28] W. A. Mustafa and H. Yazid, “Illumination and Contrast Correction Strategy using Bilateral Filtering and Binarization Comparison,” J. Telecommun. Electron. Comput. Eng., vol. 8, no. 1, pp. 67–73, 2016.

      [29] D. G. Pelli and P. Bex, “Measuring Contrast Sensitivity,” Vision Res., vol. 90, pp. 10–14, 2013.


 

View

Download

Article ID: 24792
 
DOI: 10.14419/ijet.v8i1.1.24792




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.