Salah Eddine Bekhouche

Names and affiliations of the participating systems.

Authors:

Zinelabidine Boulkenafet¹, Jukka Komulainen¹, Zahid Akhtar², Azeddine Benlamoudi³, Djamel Samai³, Salah Eddine Bekhouche⁴, Abdelkrim Ouafi⁴, Fadi Dornaika⁵, Abdelmalik Taleb-Ahmed⁶, L. Qin⁷, F. Peng⁷, L.B. Zhang⁷, M. Long⁸, S. Bhilare⁹, V. Kanhangad⁹, Artur Costa-Pazo¹⁰, Esteban Vazquez-Fernandez¹⁰, D Pérez-Cabo¹⁰, J J Moreira-Pérez¹⁰, Daniel González Jiménez¹⁰, A Mohammadi^11,12, S. Bhattacharjee¹², S. Marcel¹², S. Volkova¹³, Y. Tang¹⁴, N. Abe¹⁵, L. Li¹⁶, X. Feng¹⁶, Z. Xia¹⁶, X. Jiang¹⁶, S. Liu¹⁷, R. Shao¹⁷, P. C. Yuen¹⁷, W. R. Almeida¹⁸, F. Andal¹⁸, R. Padilha¹⁸, G. Bertocco¹⁸, W. Dias¹⁸, J. Wainer¹⁸, R. Torres¹⁸, A. Rocha¹⁸, M. A. Angeloni¹⁹, G. Folego¹⁹, A. Godoy¹⁹ and A. Hadid^1,16

1. 1. University of Oulu (FI)
   2. INRS-EMT, University of Quebec (CA)
   3. University of Ouargla (DZ)
   4. University of Biskra (DZ)
   5. University of the Basque Country (ES)
   6. University of Valenciennes (FR)
   7. Hunan University (CN)
   8. Changsha University of Science and Technology (CN)
   9. Indian Institute of Technology Indore (IN)
   10. GRADIANT (ES)
   11. Ecole Polytechnique Federale de Lausanne (CH)
   12. Idiap Research Institute (CH)
   13. FUJITSU LABORATORIES LTD LTD (JP)
   14. Vologda State University (RU)
   15. Northwestern Polytechnical University (CN)
   16. Shenzhen University (CN)
   17. Hong Kong Baptist University (HK)
   18. University of Campinas (BR)
   19. CPqD (BR)

Abstract:

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.

Please cite as:

@InProceedings{IJCB2017,
title={Personality Traits and Job Candidate Screening via Analyzing Facial Videos},
author={Bekhouche, Salah Eddine and Dornaika, Fadi and Ouafi, Abdelkrim and Taleb-Ahmed, Abdelmalik},
booktitle={Computer Vision and Pattern Recognition Workshops (CVPRW), 2017 IEEE Conference on},
pages={1660–1663},
year={2017},
organization={IEEE},
doi={10.1109/CVPRW.2017.211}
}

Downloads:

Paper: PDF
Database: OULU-NPU