This paper presents a novel deep learning-based method that integrates a Convolutional Auto-Encoder (CAE) with support vector machine (SVM) for finger-vein verification. The CAE is used to learn the features from finger-vein images, and the SVM is used to classify finger vein from these learned feature codes. The CAE consists of a finger-vein encoder, which extracts high-level feature representation from raw pixels of the images, and a decoder which outputs reconstruct finger-vein images from high-level feature code. As an effective classifier, SVM is introduced in this paper to classify the feature code which is obtained from CAE. Experiments prove that the proposed deep learning-based approach has superior performance in learning features than traditional method without any prior knowledge, presenting a good potential in the verification of finger vein.

中文翻译：

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用于指静脉验证的卷积自​​动编码器模型

本文提出了一种新的基于深度学习的方法，该方法将卷积自动编码器 (CAE) 与支持向量机 (SVM) 相结合，用于手指静脉验证。CAE用于从手指静脉图像中学习特征，SVM用于从这些学习到的特征码中对手指静脉进行分类。CAE 由手指静脉编码器和解码器组成，该编码器从图像的原始像素中提取高级特征表示，以及从高级特征代码输出重建手指静脉图像的解码器。本文引入SVM作为一种有效的分类器，对CAE得到的特征码进行分类。实验证明，所提出的基于深度学习的方法在学习特征方面比没有任何先验知识的传统方法具有更好的性能，