Palm Vein Verification System Based on SIFT Matching
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Finger vein recognition with manifold learning
Journal of Network and Computer Applications
Combination of Gabor wavelets and circular Gabor filter for finger-vein extraction
ICIC'09 Proceedings of the 5th international conference on Emerging intelligent computing technology and applications
A novel finger-vein recognition method with feature combination
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Personal identification based on finger-vein features
Computers in Human Behavior
Finger vein pattern extraction algorithm
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part I
Finger-Vein recognition based on a bank of gabor filters
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part I
Vein pattern extraction based on vectorgrams of maximal intra-neighbor difference
Pattern Recognition Letters
Chaotic random projection for cancelable biometric key generation
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
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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, the method is very fast by using the integral image method. The experimental results show the proposed method is superior to other methods in finger-vein feature extraction and solve the problem of how to extract features from obscure images efficiently. The EER of the proposed method is 0.128%.