Palmprint Classification With Multiple Filter Faces, Fourier Features and Voting Technique

Guangyi Chen; Adam Krzyzak

doi:10.5566/ias.3409

Authors

Guangyi Chen Concordia University
Adam Krzyzak

DOI:

https://doi.org/10.5566/ias.3409

Keywords:

FFT, filter faces, majority voting technique, palmprint recognition, Fast Fourier transform

Abstract

In this paper, we propose a novel method for palmprint classification. We extract the central region from the palmprint image, calculate eight filter faces (FF) from the region based on eight pairs of filters, compute the Fourier features from each FF, classify each of them to one known class, and then perform majority voting to determine the final class label of the unknown palmprint image. By examining the structures of the selected filters, we can see that our new method can suppress random noise and at the same time it can extract directional features from the palmprint images. This is the main reason why FF-based methods are better than non-FF-based methods for palmprint classification. In addition, the majority winning policy (voting) based on eight FFs improves classification accuracies significantly. Experimental results demonstrate that our new method outperforms several existing methods for palmprint classification.

Author Biographies

Guangyi Chen, Concordia University

Guang Yi Chen holds a B.Sc. in Applied Mathematics, an M.Sc. in Computing Mathematics, an M.Sc. in Computer Science, and a Ph.D. in Computer Science. During his graduate and postdoctoral studies in Canada, he was awarded many prestigious fellowships. He has published over sixty-five scientific journal papers in his fields and holds two granted USA patents in image processing. He is currently affiliated to the Department of Computer Science and Software Engineering, Concordia University, Montreal, Quebec, Canada. He is the world's top 2% scientist ranked by Stanford University. His research interests include pattern recognition, image processing, machine learning, artificial intelligence, remote sensing, and scientific computing.
Adam Krzyzak

Adam Krzyzak received the M.Sc. and Ph.D. degrees in computer engineering from the Wrocław University of Science and Technology, Poland, in 1977 and 1980, respectively, and D.Sc. degree (habilitation) in computer engineering from the Warsaw University of Technology, Poland in 1998. In 2003 he received the Title of Professor from the President of the Republic of Poland. Since 1983, he has been with the Department of Computer Science and Software Engineering, Concordia University, Montreal, Canada, where he is currently a full professor. He published over 350 papers on neural networks, pattern recognition, nonparametric estimation, image processing, computer vision and control. He has been an associate editor of IEEE Transactions on Neural Networks and IEEE Transactions on Information Theory and is presently an Associate Editor-in-Chief of the Pattern Recognition Journal. He was co-editor of the book Computer Vision and Pattern Recognition (Singapore: World Scientific, 1989) and is a co-author of the book A Distribution-Free Theory of Nonparametric Regression, New York: Springer, 2002. He is a Fellow of the IEEE and a Fellow of IAPR.

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