Impact of Neural Network Architecture for Fingerprint Recognition
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-46335-8_1
Reference18 articles.
1. Alrashidi, A., Alotaibi, A., Hussain, M., AlShehri, H., AboAlSamh, H.A., Bebis, G.: Cross-sensor fingerprint matching using siamese network and adversarial learning. Sensors 21(11) (2021). https://doi.org/10.3390/s21113657
2. Anand, V., Kanhangad, V.: PoreNet: CNN-based pore descriptor for high-resolution fingerprint recognition. IEEE Sens. J. 20(16), 9305–9313 (2020). https://doi.org/10.1109/JSEN.2020.2987287
3. Cappelli, R., Ferrara, M., Maltoni, D., Turroni, F.: Fingerprint verification competition at ijcb2011. In: 2011 International Joint Conference on Biometrics (IJCB), pp. 1–6 (2011). https://doi.org/10.1109/IJCB.2011.6117488
4. Chowdhury, A., Kirchgasser, S., Uhl, A., Ross, A.: Can a CNN automatically learn the significance of minutiae points for fingerprint matching? In: 2020 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 340–348 (2020). https://doi.org/10.1109/WACV45572.2020.9093301
5. Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), vol. 2, pp. 1735–1742 (2006). https://doi.org/10.1109/CVPR.2006.100
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