Embedded processor optimised for vascular pattern recognition

Embedded processor optimised for vascular pattern recognition

For access to this article, please select a purchase option:

Buy article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
IET Circuits, Devices & Systems — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

In this study, the authors propose an efficient embedded processing architecture that uses the vascular pattern extraction (VPE) algorithm to authenticate a user to an embedded system. This study first considers the use of direction-based vascular pattern extraction (DBVPE), and analyses the computational workload involved in running software implementations on an embedded processor. The authors then present a comprehensive performance analysis of the VPE algorithm and examine in detail the various factors that contribute to processing latencies, including VPE recognition processing. In order to improve the efficiency of VPE processing in embedded devices, the authors offer details regarding the process needed to create a highly efficient application-specific processor and extend the base instruction set of the processor by using custom instructions for recognition processing. The authors implemented our proposed methodology in the context of a commercial extensible processor design flow using the Xtensa platform from Tensilica Inc. Our experiments show that our proposed methodology achieves a 3.95-fold increase in the vascular pattern recognition speed. Hence, the authors consider our technique to be efficient.


    1. 1)
      • 1. Jain, A., Bolle, R., Pankanti, S.: ‘Biometrics persona identification in networked society’ (Kluwer Academic Publishers, 1999).
    2. 2)
      • 2. Schaumont, P., Hwang, D., Verbauwhede, I.: ‘Platform-based design for an embedded fingerprint authentication device’, Comput.-Aided Des. Integr. Circuits Syst., 2005, 24, (12), pp. 19291936 (doi: 10.1109/TCAD.2005.853709).
    3. 3)
      • 3. Pa, N.S., Chang, S.P.: ‘An embedded system for real-time facial expression recognition based on the extension theory’, Elsevier Comput. Math. Appl., 2011, 61, (8), pp. 21012106.
    4. 4)
      • 4. Chu, S.W., Yeh, M.C., Cheng, K.T.: ‘A real-time, embedded face-annotation system’. Proc. ACM Int. Conf. Multimedia, Vancouver, Canada, October 2008, pp. 989990.
    5. 5)
      • 5. Rosli, A.N.C., Ahmad, R.B., Shakaff, A.Y.M.: ‘Embedded system for biometric identification based on Iris detection’. Proc. Int. Conf. Electronic Design, Penang, Malaysia, December 2008, pp. 16.
    6. 6)
      • 6. Judith, L.J., Raul, S.R., Belen, F.S.: ‘Iris biometrics for embedded systems’, IEEE Trans. VLSI Syst., 2011, 19, (2), pp. 274282 (doi: 10.1109/TVLSI.2009.2033701).
    7. 7)
      • 7. Grabowski, K., Napieralski, A.: ‘Hardware architecture optimized for Iris recognition’, IEEE Trans. CSV T., 2011, 21, (9), pp. 12931303.
    8. 8)
      • 8. Tensilica Inc.: ‘Xtensa 8 processor’, (accessed November 2012).
    9. 9)
      • 9. Synopsys Inc.: ‘ARC cores’, (accessed November 2012).
    10. 10)
      • 10. MIPS Technologies: ‘32 and 4bit Cores’, (accessed November 2012).
    11. 11)
      • 11. Guptat, P., Ravi, S., Raghunathan, A., Jhat, N.K.: ‘Efficient fingerprint-based user authentication for embedded systems’. Proc. Design Autom. Conf., June 2005, Anaheim. USA, pp. 244247.
    12. 12)
      • 12. Aaraj, N., Ravi, S., Raghunathan, A., Jha, N.K.: ‘Hybrid architectures for efficient and secure face authentication in embedded systems’, IEEE Trans. VLSI Syst., 2007, 15, (3), pp. 269308 (doi: 10.1109/TVLSI.2007.893608).
    13. 13)
      • 13. ‘Vascular pattern recognition’, (accessed July 2012).
    14. 14)
      • 14. Kumar, A., Prathyusha, K.V.: ‘Personal authentication using hand vein triangulation and knuckle shape’, IEEE Trans. Image Process, 2009, 38, (9), pp. 21272136 (doi: 10.1109/TIP.2009.2023153).
    15. 15)
      • 15. Lin, C.L., Fan, K.C.: ‘Biometric verification using thermal images of palm-dorsa vein patterns’, IEEE Trans. Circuits Syst. Video Technol., 2004, 14, (2), pp. 199213 (doi: 10.1109/TCSVT.2003.821975).
    16. 16)
      • 16. Wang, L., Leedham, G.: ‘A thermal hand-vein pattern verification system’. Proc. Int. Conf. Advances in Pattern Recognition, Bath, UK, August 2005, pp. 5865.
    17. 17)
      • 17. Im, S.K., Park, H.M., Kim, S.W., Chung, C.K., Choi, H.S.: ‘Improved vein pattern extracting algorithm and its implementation’. Proc. Int. Conf. Consumer Electronics, Los Angles, USA, June 2000, pp. 23.
    18. 18)
      • 18. Im, S.K., Chio, H.S., Kim, S.W.: ‘A direction-based vascular pattern extraction algorithm for hand vascular pattern verification’, ETRI J., 2003, 25, (2), pp. 101108 (doi: 10.4218/etrij.03.0102.0211).
    19. 19)
      • 19. ARM Ltd.: ‘ARM926 Processor’, (accessed November 2012).
    20. 20)
      • 20. Analog Devices, Inc.: ‘ADSP-BF533’, (accessed November 2012).

Related content

This is a required field
Please enter a valid email address