Palmprint Recognition Using Multiscale Transform, Linear Discriminate Analysis, and Neural Network
Science Journal of Circuits, Systems and Signal Processing
Volume 2, Issue 5, October 2013, Pages: 112-118
Received: Oct. 21, 2013; Published: Nov. 10, 2013
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Hatem Elaydi, Electrical Engineering, the Islamic University, Gaza, Palestine
Mohanad A. M. Abukmeil, Electrical Engineering, the Islamic University, Gaza, Palestine
Mohammed Alhanjouri, Computer Engineering, the Islamic University, Gaza, Palestine
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Palmprint recognition is gaining grounds as a biometric system for forensic and commercial applications. Palmprint recognition addressed the recognition issue using low and high resolution images. This paper uses PolyU hyperspectral palmprint database, and applies back-propagation neural network for recognition, linear discriminate analysis for dimensionality reduction, and 2D discrete wavelet, ridgelet, curvelet, and contourlet for feature extraction. The recognition rate accuracy shows that contourlet outperforms other transforms.
2D Discrete Wavelet, Ridgelet, Curvelet, Contourlet, Linear Discriminate Analysis, Neural Network
To cite this article
Hatem Elaydi, Mohanad A. M. Abukmeil, Mohammed Alhanjouri, Palmprint Recognition Using Multiscale Transform, Linear Discriminate Analysis, and Neural Network, Science Journal of Circuits, Systems and Signal Processing. Vol. 2, No. 5, 2013, pp. 112-118. doi: 10.11648/j.cssp.20130205.13
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The Accessed: September, 2013. Available at:
H. Elaydi, M. Alhanjouri, and M. Abukmeil, "Palmprint recognition using 2-d wavelet, ridgelet, curvelet and contourlet," to appear in i-manager's Journal on Electrical Engineering (JEE), 2013.
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