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008			220601s2020 sz | s |||| 0|eng d
020			_a9783031018244
024	7		_a10.1007/978-3-031-01824-4 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTA1653 _b2020 EB
100	1		_aWang, Jun _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9678457
245	1	0	_aMulti-Modal Face Presentation Attack Detection _cby Jun Wan, Guodong Guo, Sergio Escalera, Hugo Jair Escalante, Stan Z. Li
250			_a1st edition 2020
264		1	_aCham _bSpringer International Publishing _c2020
300			_a1 recurso en línea (XI, 76 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Computer Vision _x2153-1064
505	0		_aPreface -- Acknowledgments -- Motivation and Background -- Multi-Modal Face Anti-Spoofing Challenge -- Review of Participants' Methods -- Challenge Results -- Conclusions and Future Works -- Bibliography -- Authors' Biographies.
520			_aFor the last ten years, face biometric research has been intensively studied by the computer vision community. Face recognition systems have been used in mobile, banking, and surveillance systems. For face recognition systems, face spoofing attack detection is a crucial stage that could cause severe security issues in government sectors. Although effective methods for face presentation attack detection have been proposed so far, the problem is still unsolved due to the difficulty in the design of features and methods that can work for new spoofing attacks. In addition, existing datasets for studying the problem are relatively small which hinders the progress in this relevant domain. In order to attract researchers to this important field and push the boundaries of the state of the art on face anti-spoofing detection, we organized the Face Spoofing Attack Workshop and Competition at CVPR 2019, an event part of the ChaLearn Looking at People Series. As part of this event, we released the largest multi-modal face anti-spoofing dataset so far, the CASIA-SURF benchmark. The workshop reunited many researchers from around the world and the challenge attracted more than 300 teams. Some of the novel methodologies proposed in the context of the challenge achieved state-of-the-art performance. In this manuscript, we provide a comprehensive review on face anti-spoofing techniques presented in this joint event and point out directions for future research on the face anti-spoofing field.
988			_aSynthesis Collection of Technology_2020
700	1		_aGuo, Guodong _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686986
700	1		_aEscalera, Sergio _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686987
700	1		_aEscalante, Hugo Jair _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686988
700	1		_aLi, S. Z., _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686989 _d1958-
776	0	8	_iPrinted edition: _z9783031000812
776	0	8	_iPrinted edition: _z9783031006968
776	0	8	_iPrinted edition: _z9783031029523
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01824-4 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2023 _dz _esc _zSI