A State-of-the-art solution for finger-vein, palm-vein, dorsal-vein recognition by using deep learning techniques
For the further details, please refer to:
[1] Kuzu, Rıdvan S., Maiorana, E. and Campisi, P. (2020), Vein-based Biometric Verification using Transfer Learning, in 43rd International Conference on Telecommunications and Signal Processing (TSP), pp. 403-409.
[2] Kuzu, Rıdvan S., Piciucco, E., Maiorana, E. and Campisi, P. (2020), On-the-Fly Finger-Vein-Based Biometric Recognition Using Deep Neural Networks, in IEEE Transactions on Information Forensics and Security, vol. 15, pp. 2641-2654, doi: 10.1109/TIFS.2020.2971144.
[3] Kuzu, Rıdvan S., Maiorana, E. and Campisi, P. (2020), Vein-Based Biometric Verification Using Densely-Connected Convolutional Autoencoder, in IEEE Signal Processing Letters, vol. 27, pp. 1869-1873, 2020, doi: 10.1109/LSP.2020.3030533.
[4] Kuzu, Rıdvan S., Maiorana, E. and Campisi, P. (2021), Loss Functions for CNN-based Biometric Vein Recognition, in 28th European Signal Processing Conference (EUSIPCO 2020), pp. 750-754.
[5] Kuzu, Rıdvan S., Maiorana, E. and Campisi, P. (2022),On the Intra-subject Similarity of Hand Vein Patterns in Biometric Recognition, in Expert Systems with Applications, 192, p.116305., doi: 10.1016/j.eswa.2021.116305.
[6] Kuzu, Rıdvan S., Maiorana, E. and Campisi, P. (2023),Gender-Specific Characteristics for Hand-Vein Biometric Recognition: Analysis and Exploitation, in IEEE Access, vol. 11, pp. 11700-11710, 2023, doi: 10.1109/ACCESS.2023.3239894.