An Efficient Dorsal Hand Vein Recognition Security System using Arduino and GSM Technology
N. Rajalakshmi1, Ramsankar M.P2, P. Manimegalai3
1N.Rajalakshmi, Associate Professor, Department of BME, KAHE, Coimbatore (Tamil Nadu), India.
2Ramsankar M.P, UG Student, Department of ECE, KAHE, Coimbatore (Tamil Nadu), India.
3P.Manimegalai, Associate Professor, Department of BME, Karunya Institute of Technology and Sciences, Coimbatore (Tamil Nadu), India.
Manuscript received on 30 September 2019 | Revised Manuscript received on 12 November 2019 | Manuscript Published on 22 November 2019 | PP: 1577-1581 | Volume-8 Issue-6S3 September 2019 | Retrieval Number: F12900986S319/19©BEIESP | DOI: 10.35940/ijeat.F1290.0986S319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: With the onset of maximum power, modest figuring and more prominent unpredictability, biometric verification has turned out to be conceivable at each scale in light of its more secure nature and furthermore easy to use conduct. Compare to other biometrics, vein biometric is a decent verification characteristic among others. The dorsal hand vein recognition is an emerging biometric procedure which is utilized for verification purposes in many applications. In this work preprocessing is done by median filter and region of interest such as veins separated from the muscles and bones through adaptive Kmeans clustering algorithm.The proposed method extracts the dorsal hand vein pattern features by using LBP and Repeated Line Tracking algorithm.Finally recognition and authentication is done using Artificial Neural Network. Arduino and GSM technology is used in this work to set security preference for the particular user.In order to validate the proposed work , a total of 480 images of dorsal hand veins is involved in this work. In a comparison with four existingverification algorithms, the proposed method achieves thehighest accuracy with lowest error rate.
Keywords: Dorsal Hand Vein, Repeated Line Tracking, LBP.
Scope of the Article: Pattern Recognition