Adaptive Learning Gabor Filter for Finger-Vein Recognition

• Published in: IEEE access Vol. 7; pp. 159821 - 159830
• Main Authors: Zhang, Yakun, Li, Weijun, Zhang, Liping, Ning, Xin, Sun, Linjun, Lu, Yaxuan
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive filters; Adaptive learning; Artificial neural networks; Back propagation; Back propagation networks; convolutional nerual networks; Design parameters; Feature extraction; Filter banks; Gabor filters; Image segmentation; Learning; Neural networks; Optimization; Recognition; Two dimensional displays; vein recognition; Veins
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2950698

Presently, finger-vein recognition is a new research direction in the field of biometric recognition. The Gabor filter has been extensively used for finger-vein recognition; however, its parameters are difficult to adjust. To solve this problem, an adaptive-learning Gabor filter is presented herein. We combine convolutional neural networks with a Gabor filter to calculate the gradient of the Gabor-filter parameters, based on the objective function, and to then optimize its parameters via back-propagation. The parameter θ of Gabor filter can be trained to the same angle as the vein texture of finger vein image. The parameter σ of Gabor filter has a certain relation with λ, and the parameter λ of Gabor filter can converge to the optimal value well. Using this method, we not only select appropriate and effective Gabor filter parameters to design the filter banks, we also consider the relationship between those parameters. Finally, we perform experiments on four public finger-vein datasets. Experimental results demonstrate that our method outperforms state-of-the-art methods in finger-vein classification.