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Human-Aided Saliency Maps Improve Generalization of Deep Learning., , and . CoRR, (2021)Human-Aided Saliency Maps Improve Generalization of Deep Learning., , and . WACV, page 1255-1264. IEEE, (2022)Explain To Me: Salience-Based Explainability for Synthetic Face Detection Models., , , , , , , and . CoRR, (2023)Are Gabor Kernels Optimal for Iris Recognition?, , and . IJCB, page 1-9. IEEE, (2020)Interpretable Deep Learning-Based Forensic Iris Segmentation and Recognition., , , , , , and . WACV (Workshops), page 359-368. IEEE, (2022)Human Saliency-Driven Patch-based Matching for Interpretable Post-mortem Iris Recognition., , , , and . WACV (Workshops), page 701-710. IEEE, (2023)CYBORG: Blending Human Saliency Into the Loss Improves Deep Learning-Based Synthetic Face Detection., , , and . WACV, page 6097-6106. IEEE, (2023)Post-Mortem Iris Recognition - A Survey and Assessment of the State of the Art., , , , , , , , , and 2 other author(s). IEEE Access, (2020)Teaching AI to Teach: Leveraging Limited Human Salience Data Into Unlimited Saliency-Based Training., , , and . BMVC, page 622. BMVA Press, (2023)Deep Learning-Based Feature Extraction in Iris Recognition: Use Existing Models, Fine-tune or Train From Scratch?, , and . BTAS, page 1-9. IEEE, (2019)