An Effective Palmprint Recognition Approach for Visible and Multispectral Sensor Images

Sensors
Abdu GumaeiAhmed Alsanad

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 app...Continue Reading

References

Apr 19, 2000·Neural Computation·N JapkowiczM A Gluck
Sep 7, 2007·Nature·Anil K Jain
Jun 6, 2012·Sensors·Xingpeng XuYafeng Li
Sep 21, 2013·IEEE Transactions on Pattern Analysis and Machine Intelligence·Ehsan Elhamifar, René Vidal
Apr 15, 2017·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society· Wai Keung Wong Yuwu Lu
Oct 11, 2017·IEEE Transactions on Cybernetics· Zhihui LaiDavid Zhang

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Methods Mentioned

BETA
feature extraction
PCA

Software Mentioned

Windows
CR
OLOF
LRRIPLD
MATLAB
DOC
RELM
CompCode
ELM

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