A Novel Optimized Deep Network for Ear Detection and Occlusion Analysis
Author:
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Computer Science Applications
Link
https://link.springer.com/content/pdf/10.1007/s11277-023-10519-9.pdf
Reference27 articles.
1. Benzaoui, A., Khaldi, Y., Bouaouina, R., Amrouni, N., Alshazly, H., & Ouahabi, A. (2023). A comprehensive survey on ear recognition: databases, approaches, comparative analysis, and open challenges. Neurocomputing. https://doi.org/10.1016/j.neucom.2023.03.040
2. Korichi, A., Slatnia, S., & Aiadi, O. (2022). TR-ICANet: A fast unsupervised deep-learning-based scheme for unconstrained ear recognition. Arabian Journal for Science and Engineering, 47(8), 9887–9898.
3. Sarangi, P. P., Mishra, B. S. P., & Dehuri, S. (2019). Fusion of PHOG and LDP local descriptors for kernel-based ear biometric recognition. Multimedia Tools and Applications, 78, 9595–9623.
4. Alshazly, H., Linse, C., Barth, E., & Martinetz, T. (2020). Deep convolutional neural networks for unconstrained ear recognition. IEEE Access, 8, 170295–170310.
5. Kamboj, A., Rani, R., & Nigam, A. (2022). A comprehensive survey and deep learning-based approach for human recognition using ear biometric. The Visual Computer, 38(7), 2383–2416.
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