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Sensors 2018, 18(5), 1575; https://doi.org/10.3390/s18051575

An Effective Palmprint Recognition Approach for Visible and Multispectral Sensor Images

1
Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
2
Department of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
*
Author to whom correspondence should be addressed.
Received: 18 April 2018 / Revised: 10 May 2018 / Accepted: 11 May 2018 / Published: 15 May 2018
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Abstract

Among several palmprint feature extraction methods the HOG-based method is attractive and performs well against changes in illumination and shadowing of palmprint images. However, it still lacks the robustness to extract the palmprint features at different rotation angles. To solve this problem, this paper presents a hybrid feature extraction method, named HOG-SGF that combines the histogram of oriented gradients (HOG) with a steerable Gaussian filter (SGF) to develop an effective palmprint recognition approach. The approach starts by processing all palmprint images by David Zhang’s method to segment only the region of interests. Next, we extracted palmprint features based on the hybrid HOG-SGF feature extraction method. Then, an optimized auto-encoder (AE) was utilized to reduce the dimensionality of the extracted features. Finally, a fast and robust regularized extreme learning machine (RELM) was applied for the classification task. In the evaluation phase of the proposed approach, a number of experiments were conducted on three publicly available palmprint databases, namely MS-PolyU of multispectral palmprint images and CASIA and Tongji of contactless palmprint images. Experimentally, the results reveal that the proposed approach outperforms the existing state-of-the-art approaches even when a small number of training samples are used. View Full-Text
Keywords: security; visible and multispectral palmprint images; HOG-SGF feature extraction; auto-encoder; regularized extreme learning machine security; visible and multispectral palmprint images; HOG-SGF feature extraction; auto-encoder; regularized extreme learning machine
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Gumaei, A.; Sammouda, R.; Al-Salman, A.M.; Alsanad, A. An Effective Palmprint Recognition Approach for Visible and Multispectral Sensor Images. Sensors 2018, 18, 1575.

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