Fast Finger Vein Recognition Based on a Dual Dimension Reduction Histogram of Oriented Gradient and Support Vector Machine
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摘要: 为减少手指静脉识别时间,提出一种双重降维方向梯度直方图特征(Histogram of Oriented Gradient,HOG)结合支持向量机(Support Vector Machine,SVM)分类的手指静脉识别方法。针对传统HOG算法特征维数高的问题,首先通过Fisher准则衡量梯度方向区间HOG特征的分类能力,然后使用序列前向选择法挑选出分类能力较优异的梯度方向区间构建部分方向区间HOG特征,最后使用主成分分析(Principal Component Analysis,PCA)降维。在公开的手指静脉数据库FV-USM和THU-FV上使用SVM多分类器进行分类识别,实验结果表明:双重降维HOG方法相较于HOG+PCA方法提取的特征维数降低了40%,识别时间减少了29.85%,识别准确率分别为99.17%和100%,等误率分别为1.07%和0.01%。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.
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Key words:
- finger vein recognition /
- HOG /
- feature selection /
- PCA /
- SVM
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表 1 核函数分类训练数据
Table 1. Training data of kernel function
Kernel Number of support vectors Accuracy MIN MAX Linear 5 8 98.67% Polynomial 8 8 75.17% RBF 8 8 97.83% Sigmoid 8 8 97.83% 表 2 时间开销比较
Table 2. Comparison of time cost
Methods Dimension of feature Extraction time/ms Recognition time/ms LBP+SVM 1152 798.64 98.32 HOG+SVM 672 25.73 35.73 HOG+PCA+SVM 145 26.76 19.10 Proposed method 87 26.97 13.59 表 3 不同方法实验数据比较
Table 3. Comparison of experimental data of different methods
Methods FV-USM THU-FV Accuracy/% EER/% Accuracy/% EER/% Maximum curvature point+MHD 87.64 10.46 98.38 2.12 Wide line detector+LTS-HD 92.38 5.35 99.26 0.34 LBP+ Euclidean distance 96.13 3.66 100 0.32 Dual dimension reduction HOG+ Euclidean distance 97.26 4.12 100 0.28 LBP+SVM 97.86 2.16 100 0.04 Proposed method 99.17 1.07 100 0.01 -
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