Fast Finger Vein Recognition Based on a Dual Dimension Reduction Histogram of Oriented Gradient and Support Vector Machine

CHU Hongjia; CHEN Guanghua; WANG Kaixuan

Volume 44 Issue 3

Mar. 2022

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CHU Hongjia, CHEN Guanghua, WANG Kaixuan. Fast Finger Vein Recognition Based on a Dual Dimension Reduction Histogram of Oriented Gradient and Support Vector Machine[J]. Infrared Technology , 2022, 44(3): 262-267.

Citation:

CHU Hongjia, CHEN Guanghua, WANG Kaixuan. Fast Finger Vein Recognition Based on a Dual Dimension Reduction Histogram of Oriented Gradient and Support Vector Machine[J]. Infrared Technology , 2022, 44(3): 262-267.

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Fast Finger Vein Recognition Based on a Dual Dimension Reduction Histogram of Oriented Gradient and Support Vector Machine

1.
Microelectronics Research & Development Center, Shanghai University, Shanghai 200444, China
2.
School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China

Received Date: 2021-01-23
Rev Recd Date: 2021-04-08
Publish Date: 2022-03-20

Abstract

Abstract

An identification model using a dual-dimension reduction histogram of oriented gradients (HOG) combined with a support vector machine (SVM) is proposed to reduce the time required for finger vein recognition. To solve the problem of high feature dimensionality in the traditional HOG algorithm, the classification ability of the gradient direction interval is first measured using the Fisher criterion. Next, the sequence forward selection method is used to select the gradient direction interval with optimal classification ability to construct a partial direction interval HOG feature. Finally, principal component analysis (PCA) is used to reduce the number of dimensions. An SVM multi-classifier was used for the classification of the FV-USM and THU-FV datasets. The experimental results demonstrate that compared to the HOG+PCA method, the feature dimensions extracted by the dual-dimensional reduction HOG method are reduced by 40%, the recognition time is reduced by 29.85%, the recognition accuracy is 99.17% and 100%, respectively, and the equal error rate is 1.07% and 0.01%, respectively.
- finger vein recognition,
- HOG,
- feature selection,
- PCA,
- SVM

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References(14)

References

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