LEADER 01078nam 2200265la 450 001 9910482701503321 005 20221108071203.0 035 $a(UK-CbPIL)2090317734 035 $a(CKB)5500000000094159 035 $a(EXLCZ)995500000000094159 100 $a20210618d1588 uy | 101 0 $adan 135 $aurcn||||a|bb| 200 10$aA B C aff bibelske Ordsprock, det er, de kortiske, lettiske oc brugeligste Sprock aff Bibelen effter A B C faar Born, P.H.R. [i.e.: Petrus Hegelund Ripensis]$b[electronic resource] 210 $aSchleswig $cNikolaus Wegener$d1588 215 $aOnline resource ([40] bl.) 300 $aReproduction of original in Det Kongelige Bibliotek / The Royal Library (Copenhagen). 700 $aHegelund$b Peder Jensen$f1542-1614.$0862116 701 $aHegelius$b Johs. Pet.$0980762 801 0$bUk-CbPIL 801 1$bUk-CbPIL 906 $aBOOK 912 $a9910482701503321 996 $aA B C aff bibelske Ordsprock, det er, de kortiske, lettiske oc brugeligste Sprock aff Bibelen effter A B C faar Born, P.H.R.$92238167 997 $aUNINA LEADER 03166nam 2200637z- 450 001 9910557721303321 005 20210501 035 $a(CKB)5400000000046111 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/69112 035 $a(oapen)doab69112 035 $a(EXLCZ)995400000000046111 100 $a20202105d2020 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aDeep Learning for Facial Informatics 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2020 215 $a1 online resource (102 p.) 311 08$a3-03936-964-4 311 08$a3-03936-965-2 330 $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. 606 $aHistory of engineering and technology$2bicssc 610 $a2D attribute maps 610 $a3D geometry data 610 $aage estimation 610 $acoarse-to-fine 610 $aconvolutional neural network 610 $aconvolutional neural network (CNN) 610 $aconvolutional neural networks 610 $adeep learning 610 $adeep metric learning 610 $adepth 610 $aemotion classification 610 $aexternal knowledge 610 $aface liveness detection 610 $afacial images processing 610 $afacial key point detection 610 $afacial landmarking 610 $afused CNN feature 610 $agenerative adversarial network 610 $aimage classification 610 $amerging networks 610 $amulti-task learning 610 $aRGB 610 $athermal image 615 7$aHistory of engineering and technology 700 $aHsu$b Gee-Sern Jison$4edt$01311357 702 $aTimofte$b Radu$4edt 702 $aHsu$b Gee-Sern Jison$4oth 702 $aTimofte$b Radu$4oth 906 $aBOOK 912 $a9910557721303321 996 $aDeep Learning for Facial Informatics$93030285 997 $aUNINA