Abstract
Finger-vein biometrics has been extensively investigated for personal verification. A challenge is that the finger-vein acquisition is affected by many factors, which results in many ambiguous regions in the finger-vein image. Generally, the separability between vein and background is poor in such regions. Despite recent advances in finger-vein pattern segmentation, current solutions still lack the robustness to extract finger-vein features from raw images because they do not take into account the complex spatial dependencies of vein pattern. This paper proposes a deep learning model to extract vein features by combining the Convolutional Neural Networks (CNN) model and Long Short-Term Memory (LSTM) model. Firstly, we automatically assign the label based on a combination of known state of the art handcrafted finger-vein image segmentation techniques, and generate various sequences for each labeled pixel along different directions. Secondly, several Stacked Convolutional Neural Networks and Long Short-Term Memory (SCNN-LSTM) models are independently trained on the resulting sequences. The outputs of various SCNN-LSTMs form a complementary and over-complete representation and are conjointly put into Probabilistic Support Vector Machine (P-SVM) to predict the probability of each pixel of being foreground (i.e., vein pixel) given several sequences centered on it. Thirdly, we propose a supervised encoding scheme to extract the binary vein texture. A threshold is automatically computed by taking into account the maximal separation between the inter-class distance and the intra-class distance. In our approach, the CNN learns robust features for vein texture pattern representation and LSTM stores the complex spatial dependencies of vein patterns. So, the pixels in any region of a test image can then be classified effectively. In addition, the supervised information is employed to encode the vein patterns, so the resulting encoding images contain more discriminating features. The experimental results on one public finger-vein database show that the proposed approach significantly improves the finger-vein verification accuracy.
Highlights
With the wide application of internal and increasing risk of terrorist attacks, information security became a hot topic and received more and more attention
Stacked Convolutional Neural Networks and Long Short-Term Memory (SCNN-LSTM) models are independently trained on the resulting sequences
Based on the supervised encoding scheme in Equation (15), we can extract the finger-vein patterns from the probability map which is computed by the proposed SCNN-LSTM approach
Summary
With the wide application of internal and increasing risk of terrorist attacks, information security became a hot topic and received more and more attention. Various biometric characteristics such as fingerprints [1], palm-print [2], finger-vein [3,4], hand-vein [5], palm-vein [6], face [7], iris [8], voice [9], signature [10] have been employed for verification and can be broadly classified into two. (1) Extrinsic characteristics (e.g., fingerprints, palm-print, face, voice, signature); (2) Intrinsic characteristics (e.g., finger-vein, palm-vein, hand-vein). The extrinsic characteristics are prone to be attacked because faked face and fingerprint can successfully cheat the verification system [11]. As the intrinsic characteristics such as finger-vein conceal the skin and not copied and forged, they show high security and privacy in practical application
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