A competition on generalized software-based face presentation attack detection in mobile scenarios
| Autor: | Guilherme Folego, Daniel Perez-Cabo, Fei Peng, Le-Bing Zhang, Zahid Akhtar, Sushil Bhattacharjee, Zinelabidine Boulkenafet, S. Volkova, Amir H. Mohammadi, Xiaoyi Feng, Azeddine Benlamoudi, Lei Li, Shruti Bhilare, Abdelkrim Ouafi, L. Qin, Rafael Padilha, Salah Eddine Bekhouche, S. Liu, William Dias, Marcus de Assis Angeloni, Pong C. Yuen, Daniel González-Jiménez, Rui Shao, Ricardo da Silva Torres, Jukka Komulainen, Waldir Rodrigues De Almeida, X. Jiang, Vivek Kanhangad, J. J. Moreira-Perez, Abdenour Hadid, Djamel Samai, Anderson Rocha, Esteban Vazquez-Fernandez, Fernanda A. Andaló, Abdelmalik Taleb-Ahmed, Alan Godoy, Zhaoqiang Xia, Y. Tang, Sébastien Marcel, N. Abe, Fadi Dornaika, Gabriel Bertocco, Artur Costa-Pazo, Jacques Wainer, M. Long |
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| Rok vydání: | 2017 |
| Předmět: |
021110 strategic
defence & security studies Generalization business.industry Computer science Face Presentation Liveness Feature extraction 0211 other engineering and technologies 02 engineering and technology Machine learning computer.software_genre Software Feature (computer vision) Face (geometry) 0202 electrical engineering electronic engineering information engineering sort 020201 artificial intelligence & image processing Artificial intelligence business computer |
| Zdroj: | IJCB |
| DOI: | 10.1109/btas.2017.8272758 |
| Popis: | In recent years, software-based face presentation attack detection (PAD) methods have seen a great progress. However, most existing schemes are not able to generalize well in more realistic conditions. The objective of this competition is to evaluate and compare the generalization performances of mobile face PAD techniques under some real-world variations, including unseen input sensors, presentation attack instruments (PAI) and illumination conditions, on a larger scale OULU-NPU dataset using its standard evaluation protocols and metrics. Thirteen teams from academic and industrial institutions across the world participated in this competition. This time typical liveness detection based on physiological signs of life was totally discarded. Instead, every submitted system relies practically on some sort of feature representation extracted from the face and/or background regions using hand-crafted, learned or hybrid descriptors. Interesting results and findings are presented and discussed in this paper. |
| Databáze: | OpenAIRE |
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