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Multispectral hand features for secure biometric authentication systems

机译:用于安全生物识别认证系统的多光谱手功能

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With rapid growth of mobile-based computing, reliable security and user authentication methods are necessary. This article proposes a proof-of-concept biometric authentication method utilizing hand images collected in different light spectra. An analysis of similarity between pattern of blood vessels extracted from near-infrared images and thermal images and assessment of the correlation between individual biometric features contained in each image type is also performed. Results indicate a large potential of using thermal images in biometrics more extensively than now. Also proposed and evaluated are biometric recognition methods based on images of the hand acquired in visible light, near infrared, and using thermal infrared sensors. Two approaches were used to assess information content in images of each type: one based on texture descriptor and one employing convolutional neural networks. In an evaluation gathering data from 104 subjects, the former yielded the lowest equal error rate (EER) of 7.44%, whereas the latter approach gave EER=0.03% for thermal images. Finally, fusion of different-spectra modalities increases accuracy and further reduces EER to 0.01%. This is, to the authors' best knowledge, the first study exploring the concept of fusing different spectral representations of the human hand for the purpose of biometric recognition.
机译:随着基于移动的计算机的快速增长,需要可靠的安全性和用户身份验证方法。本文提出了利用在不同光谱中收集的手图像的概念验证生物识别方法。还执行了从近红外图像和热图像中提取的血管模式的相似性分析,以及每种图像类型中包含的各个生物特征之间的相关性的评估。结果表明,比现在更广泛地使用生物识别材料的热图像的大潜力。还提出和评估是基于在可见光,近红外线和使用热红外传感器中获得的手的图像的生物识别方法。使用两种方法来评估每种类型的图像中的信息内容:一个基于纹理描述符和采用卷积神经网络的图像。在从104个受试者的评估数据中,前者产生了7.44%的最低差错率(eer),而后一种方法给出了eer = 0.03%的热图像。最后,不同光谱模式的融合会提高精度,并进一步降低EER至0.01%。这是对提交人的最佳知识,第一研究探索了用于生物识别目的的融合人手的不同光谱表示的概念。

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