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 , whereas the latter approach gave for thermal images. Finally, fusion of different-spectra modalities increases accuracy and further reduces EER to . 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.

中文翻译：

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用于安全生物特征认证系统的多光谱手部特征

随着基于移动计算的快速增长，需要可靠的安全性和用户身份验证方法。本文提出了一种利用在不同光谱中收集的手部图像的概念验证生物特征认证方法。还对从近红外图像和热图像中提取的血管模式之间的相似性进行了分析，并评估了每种图像类型中包含的各个生物特征之间的相关性。结果表明，比现在更广泛地在生物识别技术中使用热图像具有巨大的潜力。还提出并评估了基于在可见光、近红外和使用热红外传感器中获取的手部图像的生物特征识别方法。使用两种方法来评估每种类型图像中的信息内容：一种基于纹理描述符，一种采用卷积神经网络。在从 104 名受试者收集数据的评估中，前者产生的最低等错误率 (EER) 为，而后一种方法用于热图像。最后，不同光谱模式的融合提高了准确性，并进一步将 EER 降低到. 据作者所知，这是第一项探索融合人手的不同光谱表示以进行生物识别的概念的研究。