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Deep Learning for Biometric Face Recognition: Experimental Study on Benchmark Data Sets.

, , , , , , and . Deep Biometrics, Springer International Publishing, (2020)

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Benchmarking of Fully Connected and Convolutional Neural Networks on Face Recognition: Case of Makeup and Occlusions., , and . SAI (3), volume 285 of Lecture Notes in Networks and Systems, page 270-285. Springer, (2021)Using Statistical and Artificial Neural Networks Meta-learning Approaches for Uncertainty Isolation in Face Recognition by the Established Convolutional Models., , and . LOD, volume 13164 of Lecture Notes in Computer Science, page 338-352. Springer, (2021)Learning Incorrect Verdict Patterns of the Established Face Recognizing CNN Models Using Meta-Learning Supervisor ANN., , and . IntelliSys (2), volume 295 of Lecture Notes in Networks and Systems, page 287-304. Springer, (2021)Loss Function with Memory for Trustworthiness Threshold Learning: Case of Face and Facial Expression Recognition., and . LOD (1), volume 13810 of Lecture Notes in Computer Science, page 78-92. Springer, (2022)Isolating Uncertainty of the Face Expression Recognition with the Meta-Learning Supervisor Neural Network., , and . AIVR, page 104-112. ACM, (2021)Deep Learning for Biometric Face Recognition: Experimental Study on Benchmark Data Sets., , , , , , and . Deep Biometrics, Springer International Publishing, (2020)Challenges in Real-Life Face Recognition with Heavy Makeup and Occlusions Using Deep Learning Algorithms., , and . LOD (2), volume 12566 of Lecture Notes in Computer Science, page 600-611. Springer, (2020)Challenges in Face Recognition Using Machine Learning Algorithms: Case of Makeup and Occlusions., , and . IntelliSys (2), volume 1251 of Advances in Intelligent Systems and Computing, page 86-102. Springer, (2020)