| 000 | 03318naaaa2200589uu 4500 | ||
|---|---|---|---|
| 001 | https://directory.doabooks.org/handle/20.500.12854/69112 | ||
| 005 | 20220714164242.0 | ||
| 020 | _abooks978-3-03936-965-2 | ||
| 020 | _a9783039369645 | ||
| 020 | _a9783039369652 | ||
| 024 | 7 |
_a10.3390/books978-3-03936-965-2 _cdoi |
|
| 041 | 0 | _aEnglish | |
| 042 | _adc | ||
| 072 | 7 |
_aTBX _2bicssc |
|
| 100 | 1 |
_aHsu, Gee-Sern Jison _4edt _91579412 |
|
| 700 | 1 |
_aTimofte, Radu _4edt _91579413 |
|
| 700 | 1 |
_aHsu, Gee-Sern Jison _4oth _91579412 |
|
| 700 | 1 |
_aTimofte, Radu _4oth _91579413 |
|
| 245 | 1 | 0 | _aDeep Learning for Facial Informatics |
| 260 |
_aBasel, Switzerland _bMDPI - Multidisciplinary Digital Publishing Institute _c2020 |
||
| 300 | _a1 electronic resource (102 p.) | ||
| 506 | 0 |
_aOpen Access _2star _fUnrestricted online access |
|
| 520 | _aDeep learning has been revolutionizing many fields in computer vision, and facial informatics is one of the major fields. Novel approaches and performance breakthroughs are often reported on existing benchmarks. As the performances on existing benchmarks are close to saturation, larger and more challenging databases are being made and considered as new benchmarks, further pushing the advancement of the technologies. Considering face recognition, for example, the VGG-Face2 and Dual-Agent GAN report nearly perfect and better-than-human performances on the IARPA Janus Benchmark A (IJB-A) benchmark. More challenging benchmarks, e.g., the IARPA Janus Benchmark A (IJB-C), QMUL-SurvFace and MegaFace, are accepted as new standards for evaluating the performance of a new approach. Such an evolution is also seen in other branches of face informatics. In this Special Issue, we have selected the papers that report the latest progresses made in the following topics: 1. Face liveness detection 2. Emotion classification 3. Facial age estimation 4. Facial landmark detection We are hoping that this Special Issue will be beneficial to all fields of facial informatics. | ||
| 540 |
_aCreative Commons _fhttps://creativecommons.org/licenses/by/4.0/ _2cc _4https://creativecommons.org/licenses/by/4.0/ |
||
| 546 | _aEnglish | ||
| 650 | 7 |
_aHistory of engineering & technology _2bicssc _91129967 |
|
| 653 | _adeep learning | ||
| 653 | _aRGB | ||
| 653 | _adepth | ||
| 653 | _afacial landmarking | ||
| 653 | _amerging networks | ||
| 653 | _a3D geometry data | ||
| 653 | _a2D attribute maps | ||
| 653 | _afused CNN feature | ||
| 653 | _acoarse-to-fine | ||
| 653 | _aconvolutional neural network (CNN) | ||
| 653 | _adeep metric learning | ||
| 653 | _amulti-task learning | ||
| 653 | _aimage classification | ||
| 653 | _aage estimation | ||
| 653 | _agenerative adversarial network | ||
| 653 | _aemotion classification | ||
| 653 | _afacial key point detection | ||
| 653 | _afacial images processing | ||
| 653 | _aconvolutional neural networks | ||
| 653 | _aface liveness detection | ||
| 653 | _aconvolutional neural network | ||
| 653 | _athermal image | ||
| 653 | _aexternal knowledge | ||
| 856 | 4 | 0 |
_awww.oapen.org _uhttps://mdpi.com/books/pdfview/book/2884 _70 _zDOAB: download the publication |
| 856 | 4 | 0 |
_awww.oapen.org _uhttps://directory.doabooks.org/handle/20.500.12854/69112 _70 _zDOAB: description of the publication |
| 999 |
_c2984705 _d2984705 |
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