Analysis of Vascular Pattern Recognition Using Neural Network

Full Text (PDF, 607KB), PP.9-19

Views: 0 Downloads: 0


Navjot Kaur 1 Amardeep Singh 1

1. Punjabi University, Patiala (147002), India

* Corresponding author.


Received: 31 May 2015 / Revised: 4 Jul. 2015 / Accepted: 10 Aug. 2015 / Published: 8 Sep. 2015

Index Terms

Neural Network, Scale Invariant Feature Transform, False Acceptation Rate, False Rejection Rate, Canny Edge Detector


Biometric identification using vein patterns is a recent technique. The vein patterns are unique to each individual even in twins and they don't change over age except their size. As veins are beneath the skin it is difficult to forge. BOSPHOROUS hand vein database is used in this work. Hand vein images are uploaded first and key points using Scale Invariant Feature Transform (SIFT) are extracted. Then the neural network is used for training these images. Finally neural network is used for testing these images to check whether the image used for testing matches with the existing database or not. Results are computed like False Acceptation Rate (FAR), False Rejection Rate (FRR), accuracy and error per bit stream.

Cite This Paper

Navjot Kaur, Amardeep Singh ,"Analysis of Vascular Pattern Recognition Using Neural Network", International Journal of Mathematical Sciences and Computing(IJMSC), Vol.1, No.3, pp.9-19, 2015.DOI: 10.5815/ijmsc.2015.03.02


[1] J. M. Cross and C. L. Smith. Thermographic Imaging of the Subcutaneous Vascular Network of the Back of the Hand for Biometric Identification, Security Technology. Proceedings. Institute of Electrical and Electronics Engineers 29th Annual 1995 International Carnahan Conference, IEEE, ISBN: 0-7803-2627-X, pp. 20-35, 1995.

[2] A K Jain, A Ross and S Prabhakar. An Introduction to Biometric Recognition, Circuits and Systems for Video Technology, IEEE Trans, ISSN: 1051-8215, Vol. 14, pp. 4-20, 2004.

[3] Toshiyuki Tanaka, Naohiko Kubo. Biometric Authentication by Hand Vein Patterns, SICE Annual Conference, Sapporo, ISBN: 4-907764-22-7, Vol.1, pp. 249- 253, Aug 2004.

[4] E. Yourk et al. Hand Geometry, Image and Vision Computing, 24(5), 483-497,2006.

[5] Di Huang1, et al. Hand Vein Recognition based on Oriented Gradient Maps and Local Feature Matching, Computer Vision – ACCV 2012 , Springer, ISBN: 978-3-642-37446-3 , Vol.7727, pp. 430-444, 2007.

[6] Mohamed Shahin et al. Biometric Authentication Using Fast Correlation of Near Infrared Hand Vein Patterns , World Academy of Science, Engineering and Technology, Vol.2, pp.141-148, 2008.

[7] Raman Maini & Dr. Himanshu Aggarwal. Study and Comparison of Various Image Edge Detection Techniques, International Journal of Image Processing (IJIP), Volume (3) : Issue (1) 1,2009.

[8] Chetana Hegde et al. Authentication of damaged hand vein patterns by modularization TENCON IEEE Region 10 Conference, IEEE, ISBN: 978-1-4244-4546-2 , pp. 1-6, 2009.

[9] G.T. Shrivakshan, Dr.C. Chandrasekar. A Comparison of various Edge Detection Techniques used in Image Processing, IJCSI International Journal of Computer Science, Issues, Vol. 9, Issue 5, 2012.

[10] S.Manikanda prabu, S.N. Sivanandam. A Novel Biometric system for Person Recognition Using Palm vein Images, International Journal on Computer Science and Engineering (IJCSE), ISSN:0975-3397, Vol. 5 , 2013.

[11] A. Muthukumar and S. Kannan, "Finger Knuckle Print Recognition with SIFT and K-means Algorithm", ICTACT Journal on Image and Video Processing, ISSN: 0976-9102, VOLUME: 03, ISSUE: 03, 2013.

[12] V.Krishna Sree, P.Sudhakar Rao. Dorsal Hand Vein Pattern Authentication by Hough Peaks, IJRET: International Journal of Research in Engineering and Technology, ISSN: 2321-7308, Vol.3, pp.16-22,2014.

[13] N. V. Krishnaveni et al. Personal Authentication Using Hand Vein, International Journal of Engineering Research & Technology (IJERT), ISSN: 2278-0181, Vol. 2, pp. 2333

[14] Rafael C. Gonzalez, Richard E. Woods, "Image Segmentation," in Digital Image Processing, ISBN 978-81-317-2695-2,Third Edition, Dorling Kindersley India Pvt. Ltd.