000 04083nam a22005775i 4500
001 978-3-031-01824-4
003 DE-He213
005 20240730163725.0
007 cr nn 008mamaa
008 220601s2020 sz | s |||| 0|eng d
020 _a9783031018244
_9978-3-031-01824-4
024 7 _a10.1007/978-3-031-01824-4
_2doi
050 4 _aTA1501-1820
050 4 _aTA1634
072 7 _aUYT
_2bicssc
072 7 _aCOM016000
_2bisacsh
072 7 _aUYT
_2thema
082 0 4 _a006
_223
100 1 _aWan, Jun.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_980196
245 1 0 _aMulti-Modal Face Presentation Attack Detection
_h[electronic resource] /
_cby Jun Wan, Guodong Guo, Sergio Escalera, Hugo Jair Escalante, Stan Z. Li.
250 _a1st ed. 2020.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2020.
300 _aXI, 76 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _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.
650 0 _aImage processing
_xDigital techniques.
_94145
650 0 _aComputer vision.
_980197
650 0 _aPattern recognition systems.
_93953
650 1 4 _aComputer Imaging, Vision, Pattern Recognition and Graphics.
_931569
650 2 4 _aComputer Vision.
_980198
650 2 4 _aAutomated Pattern Recognition.
_931568
700 1 _aGuo, Guodong.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_980199
700 1 _aEscalera, Sergio.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_980200
700 1 _aEscalante, Hugo Jair.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_980201
700 1 _aLi, Stan Z.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_980202
710 2 _aSpringerLink (Online service)
_980203
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031000812
776 0 8 _iPrinted edition:
_z9783031006968
776 0 8 _iPrinted edition:
_z9783031029523
830 0 _aSynthesis Lectures on Computer Vision,
_x2153-1064
_980204
856 4 0 _uhttps://doi.org/10.1007/978-3-031-01824-4
912 _aZDB-2-SXSC
942 _cEBK
999 _c84916
_d84916