On the Robustness of Deep Learning Based Face Recognition

Author:

Bailer Werner1,Winter Martin1

Affiliation:

1. JOANNEUM RESEARCH, Graz, Austria

Funder

European Commission H2020

Publisher

ACM Press

Reference18 articles.

1. Leo Breimann. 2001. Random Forests. Machine Learning , Vol. 45 (2001), 5--32.

2. Antonio Criminisi, Jamie Shotton, and Ender Konukoglu. 2012. Decision Forests: A Unified Framework for Classification, Regression, Density Estimation, Manifold Learning and Semi-Supervised Learning. Found. Trends. Comput. Graph. Vis. , Vol. 7 (Feb. 2012), 81--227. https://doi.org/10.1561/0600000035

3. Nilaksh Das, Madhuri Shanbhogue, Shang-Tse Chen, Fred Hohman, Siwei Li, Li Chen, Michael E Kounavis, and Duen Horng Chau. 2018. Shield: Fast, practical defense and vaccination for deep learning using jpeg compression. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 196--204.

4. Samuel Dodge and Lina Karam. 2016. Understanding how image quality affects deep neural networks. In 2016 eighth international conference on quality of multimedia experience (QoMEX). IEEE, 1--6.

5. Manuel Günther, Steve Cruz, Ethan M Rudd, and Terrance E Boult. 2017. Toward open-set face recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. 71--80.

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1. Comparative Analysis of Face Recognition Algorithms: Evaluating LBPH and FaceNet with Haar Cascade;2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT);2023-07-06

2. AI4TV 2019;Proceedings of the 27th ACM International Conference on Multimedia;2019-10-15

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