Abstract
The finger vein biometric offers the perfect balance between security and economic viability and thus has gained a lot of attention in recent years, offering benefits over other conventional methods such as being least susceptible to identity theft as veins are present beneath the human skin, being unaffected by ageing of the person, etc. These reasons provide enough motivation to develop working models that would solve the ever-increasing need for security. In this paper, we have investigated the finger vein recognition problem. We have used deep convolutional neural network models for feature extraction purposes on two commonly used publicly available finger vein datasets. To improve the performance further on unseen data for verification purposes, we have employed one-shot learning model namely the Triplet loss network model and evaluated its performance. The extensive set of experiments that we have conducted yield classification and correct identification accuracies in ranges upwards of 95% and equal error rates less than 4%.
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Chawla, B., Tyagi, S., Jain, R., Talegaonkar, A., Srivastava, S. (2021). Finger Vein Recognition Using Deep Learning. In: Bansal, P., Tushir, M., Balas, V., Srivastava, R. (eds) Proceedings of International Conference on Artificial Intelligence and Applications. Advances in Intelligent Systems and Computing, vol 1164. Springer, Singapore. https://doi.org/10.1007/978-981-15-4992-2_7
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DOI: https://doi.org/10.1007/978-981-15-4992-2_7
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