000 | 04083nam a22005775i 4500 | ||
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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 |
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024 | 7 |
_a10.1007/978-3-031-01824-4 _2doi |
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050 | 4 | _aTA1501-1820 | |
050 | 4 | _aTA1634 | |
072 | 7 |
_aUYT _2bicssc |
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072 | 7 |
_aCOM016000 _2bisacsh |
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072 | 7 |
_aUYT _2thema |
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082 | 0 | 4 |
_a006 _223 |
100 | 1 |
_aWan, Jun. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _980196 |
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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. |
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300 |
_aXI, 76 p. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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490 | 1 |
_aSynthesis Lectures on Computer Vision, _x2153-1064 |
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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 |
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650 | 0 |
_aComputer vision. _980197 |
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650 | 0 |
_aPattern recognition systems. _93953 |
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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 |
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700 | 1 |
_aEscalera, Sergio. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _980200 |
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700 | 1 |
_aEscalante, Hugo Jair. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _980201 |
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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 |