基于自回归模型的手背静脉活动性检测

Yiding Wang, Qi Qi, Kefeng Li
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引用次数: 1

摘要

提出了一种基于AR模型的手背静脉活动性检测方法。首先,基于修正主成分分析(PCA),利用已有的真实DHV图像构成投影空间;与以往使用PCA方法的研究不同,本文采用零特征值及其特征向量构成投影空间。其次,将真实和假DHV图像的测试样本投影到投影空间中,生成一维向量,提取其噪声信息;然后,通过估计向量的功率谱,对每个测试样本建立自回归(AR)模型,检测DHV的活性。在一个包含510张真实DHV图像和300张3种不同类型的假DHV图像的数据库上对该方法进行了测试。实验结果表明,该方法具有良好的识别率,平均识别率达99%。
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Liveness detection of dorsal hand vein based on AutoRegressive model
A novel method for liveness detection of dorsal hand vein (DHV) based on AR model is proposed. Firstly, existing real DHV images are used to constitute a projection space based on modified principal component analysis (PCA). Unlike the previous works using the method of PCA, zero eigenvalues with their eigenvectors are used to constitute the projection space in this work. Secondly, test samples, including both real and fake DHV images, are projected to the projection space to produce one-dimensional vectors to extract their noise information. Then, autoregressive (AR) model is established for each test sample by estimating the power spectrum of the vector to detect the liveness of DHV. The proposed method is tested on a database of 510 real DHV images and 300 fake DHV images of 3 different types. The experimental results show that the proposed method performs well with an average recognition rate of 99%.
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