Multiscale Feature Extraction of Finger-Vein Patterns Based on Curvelets and Local Interconnection Structure Neural Network

In this paper, we originally propose a multiscale feature extraction method of finger-vein patterns based on curvelets and local interconnection structure neural networks. The curvelets is used to perform the multiscale self-adaptive enhancement transform on the finger-vein image and a neural network with local interconnection structure is designed to extract the features of the finger-vein pattern. This method has the following features: Firstly, the feature of finger-vein is line feature, or anisotropy, which is more suitable to be processed by curvelets than wavelets, especially when dealing with the obscure anisotropic features. Secondly, when the multiscale self-adaptive enhancement transform is applied to the finger-vein image, the finger-vein pattern is emphasized and noises are refrained greatly. Thirdly, a local interconnection neural network with linear receptive field is designed to deal with finger-vein patterns of different thickness and capture the patterns. Fourthly, th...

• Type: Conference
• Year: 2006
• Where: ICPR
• Authors: Zhongbo Zhang, Siliang Ma, Xiao Han
• Keywords: Computer Vision, Finger-vein Patterns, ICPR 2006, Local Interconnection, Multiscale Self-adaptive Enhancement