Ms. Shuyi Li Profile

Shuyi Li

at China Civil Aviation Univ

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Proceedings Article | 6 May 2019 Paper

Finger vein recognition based on local graph structural coding and CNN

Shuyi Li, Haigang Zhang, Jinfeng Yang

Proceedings Volume 11069, 110693I (2019) https://doi.org/10.1117/12.2524152

KEYWORDS: Veins, Convolution, Feature extraction, Databases, Binary data, Data modeling, Image compression, Performance modeling, Image classification, Facial recognition systems

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In recent years, deep learning has received an excellent performance in the tasks of image feature extraction and image classification. Besides, the coding-based methods have been widely focused on because of their outstanding local description. In this paper, we propose a novel method for finger-vein recognition, which combines local coding and convolution neural network (LC-CNN). Based on local graph structure (LGS), a weighted symmetrical LGS is firstly proposed to locally represent the gradient relationship among the surrounding pixels. Then, the traditional local coding methods are reconstructed with a set of fixed sparse predefined binary convolution filters. To address the over-fitting of the network, we use the local coding convolution to alter standard convolution in pre-trained CNN. Finally, the extracted feature vector are input into a support vector machine (SVM) for images classification. Experimental results show that the proposed approach achieves better performance than the traditional coding methods on finger vein recognition.

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