Deep Learning for Facial Informatics

Deep 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 chal...

Full description

Saved in:
Bibliographic Details
Other Authors: Hsu, Gee-Sern Jison (Editor), Timofte, Radu (Editor)
Format: Electronic Book Chapter
Language:English
Published: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2020
Subjects:
Online Access:DOAB: download the publication
DOAB: description of the publication
Tags: Add Tag
No Tags, Be the first to tag this record!

MARC

LEADER 00000naaaa2200000uu 4500
001 doab_20_500_12854_69112
005 20210501
003 oapen
006 m o d
007 cr|mn|---annan
008 20210501s2020 xx |||||o ||| 0|eng d
020 |a books978-3-03936-965-2 
020 |a 9783039369645 
020 |a 9783039369652 
040 |a oapen  |c oapen 
024 7 |a 10.3390/books978-3-03936-965-2  |c doi 
041 0 |a eng 
042 |a dc 
072 7 |a TBX  |2 bicssc 
100 1 |a Hsu, Gee-Sern Jison  |4 edt 
700 1 |a Timofte, Radu  |4 edt 
700 1 |a Hsu, Gee-Sern Jison  |4 oth 
700 1 |a Timofte, Radu  |4 oth 
245 1 0 |a Deep Learning for Facial Informatics 
260 |a Basel, Switzerland  |b MDPI - Multidisciplinary Digital Publishing Institute  |c 2020 
300 |a 1 electronic resource (102 p.) 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
506 0 |a Open Access  |2 star  |f Unrestricted online access 
520 |a Deep 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 |a Creative Commons  |f https://creativecommons.org/licenses/by/4.0/  |2 cc  |4 https://creativecommons.org/licenses/by/4.0/ 
546 |a English 
650 7 |a History of engineering & technology  |2 bicssc 
653 |a deep learning 
653 |a RGB 
653 |a depth 
653 |a facial landmarking 
653 |a merging networks 
653 |a 3D geometry data 
653 |a 2D attribute maps 
653 |a fused CNN feature 
653 |a coarse-to-fine 
653 |a convolutional neural network (CNN) 
653 |a deep metric learning 
653 |a multi-task learning 
653 |a image classification 
653 |a age estimation 
653 |a generative adversarial network 
653 |a emotion classification 
653 |a facial key point detection 
653 |a facial images processing 
653 |a convolutional neural networks 
653 |a face liveness detection 
653 |a convolutional neural network 
653 |a thermal image 
653 |a external knowledge 
856 4 0 |a www.oapen.org  |u https://mdpi.com/books/pdfview/book/2884  |7 0  |z DOAB: download the publication 
856 4 0 |a www.oapen.org  |u https://directory.doabooks.org/handle/20.500.12854/69112  |7 0  |z DOAB: description of the publication