Convolutional Auto-Encoder Based Deep Feature Learning for Finger-Vein Verification

摘要

This paper presents a novel deep learning-based method that integrates a Convolutional Auto-Encoder (CAE) with Convolutional Neural Network (CNN) for finger vein verification. The CAE is used to learn the feature codes from finger vein images and the CNN 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. Experimental study proves that the proposed deep learning based method has superior performance in learning features than traditional method without any prior knowledge, presenting a good potential in the verification of finger vein.