Paper
30 September 2011 Vein matching using artificial neural network in vein authentication systems
Azadeh Noori Hoshyar, Riza Sulaiman
Author Affiliations +
Proceedings Volume 8285, International Conference on Graphic and Image Processing (ICGIP 2011); 82850Z (2011) https://doi.org/10.1117/12.913380
Event: 2011 International Conference on Graphic and Image Processing, 2011, Cairo, Egypt
Abstract
Personal identification technology as security systems is developing rapidly. Traditional authentication modes like key; password; card are not safe enough because they could be stolen or easily forgotten. Biometric as developed technology has been applied to a wide range of systems. According to different researchers, vein biometric is a good candidate among other biometric traits such as fingerprint, hand geometry, voice, DNA and etc for authentication systems. Vein authentication systems can be designed by different methodologies. All the methodologies consist of matching stage which is too important for final verification of the system. Neural Network is an effective methodology for matching and recognizing individuals in authentication systems. Therefore, this paper explains and implements the Neural Network methodology for finger vein authentication system. Neural Network is trained in Matlab to match the vein features of authentication system. The Network simulation shows the quality of matching as 95% which is a good performance for authentication system matching.
© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Azadeh Noori Hoshyar and Riza Sulaiman "Vein matching using artificial neural network in vein authentication systems", Proc. SPIE 8285, International Conference on Graphic and Image Processing (ICGIP 2011), 82850Z (30 September 2011); https://doi.org/10.1117/12.913380
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Cited by 2 scholarly publications.
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KEYWORDS
Veins

Neural networks

Biometrics

Artificial neural networks

Pattern recognition

Security technologies

Feature extraction

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