Ensemble convolutional neural networks and transformer-based segmentation methods for achieving accurate sclera segmentation in eye images
Author:
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Signal Processing
Link
https://link.springer.com/content/pdf/10.1007/s11760-023-02891-7.pdf
Reference25 articles.
1. Rot, P., Vitek, M., Grm, K., Emeršič, Ž, Peer, P., Štruc, V.: Deep sclera segmentation and recognition. In: Handbook of Vascular Biometrics, pp. 395–432. Springer (2020)
2. Vitek, M., Hafner, A., Peer, P., Jaklič, A.: Evaluation of deep approaches to sclera segmentation. In: 2021 44th International Convention on Information, Communication and Electronic Technology (MIPRO), pp. 1097–1102. IEEE (2021)
3. Vitek, M., Rot, P., Štruc, V., Peer, P.: A comprehensive investigation into sclera biometrics: a novel dataset and performance study. Neural Comput. Appl.Comput. Appl. 32, 17941–17955 (2020)
4. Jain, A.K., Ross, A.A., Nandakumar, K.: Introduction to Biometrics. Springer Science & Business Media (2011)
5. Naqvi, R.A., Loh, W.K.: Sclera-net: accurate sclera segmentation in various sensor images based on the residual encoder and decoder network. IEEE Access 7, 98208–98227 (2019)
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