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...
Αποθηκεύτηκε σε:
| Μορφή: | Online |
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| Γλώσσα: | Αγγλικά |
| Έκδοση: |
MDPI - Multidisciplinary Digital Publishing Institute
2021
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| Θέματα: | |
| Διαθέσιμο Online: | ONIX_20210501_9783039369645_858 |
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| _version_ | 1869521804393971712 |
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| collection | Directory of Open Access Books |
| description | 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. |
| format | Online |
| id | doab-20.500.12854ir-69112 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-691122024-04-11T15:10:17Z Deep Learning for Facial Informatics Hsu, Gee-Sern Jison Timofte, Radu deep learning RGB depth facial landmarking merging networks 3D geometry data 2D attribute maps fused CNN feature coarse-to-fine convolutional neural network (CNN) deep metric learning multi-task learning image classification age estimation generative adversarial network emotion classification facial key point detection facial images processing convolutional neural networks face liveness detection convolutional neural network thermal image external knowledge thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology 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. 2021-05-01T15:41:23Z 2021-05-01T15:41:23Z 2020 book ONIX_20210501_9783039369645_858 9783039369645 9783039369652 https://directory.doabooks.org/handle/20.500.12854/69112 eng application/octet-stream Attribution 4.0 International https://mdpi.com/books/pdfview/book/2884 https://mdpi.com/books/pdfview/book/2884 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-03936-965-2 10.3390/books978-3-03936-965-2 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783039369645 9783039369652 102 Basel, Switzerland open access |
| spellingShingle | deep learning RGB depth facial landmarking merging networks 3D geometry data 2D attribute maps fused CNN feature coarse-to-fine convolutional neural network (CNN) deep metric learning multi-task learning image classification age estimation generative adversarial network emotion classification facial key point detection facial images processing convolutional neural networks face liveness detection convolutional neural network thermal image external knowledge thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology Deep Learning for Facial Informatics |
| title | Deep Learning for Facial Informatics |
| title_full | Deep Learning for Facial Informatics |
| title_fullStr | Deep Learning for Facial Informatics |
| title_full_unstemmed | Deep Learning for Facial Informatics |
| title_short | Deep Learning for Facial Informatics |
| title_sort | deep learning for facial informatics |
| topic | deep learning RGB depth facial landmarking merging networks 3D geometry data 2D attribute maps fused CNN feature coarse-to-fine convolutional neural network (CNN) deep metric learning multi-task learning image classification age estimation generative adversarial network emotion classification facial key point detection facial images processing convolutional neural networks face liveness detection convolutional neural network thermal image external knowledge thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology |
| topic_facet | deep learning RGB depth facial landmarking merging networks 3D geometry data 2D attribute maps fused CNN feature coarse-to-fine convolutional neural network (CNN) deep metric learning multi-task learning image classification age estimation generative adversarial network emotion classification facial key point detection facial images processing convolutional neural networks face liveness detection convolutional neural network thermal image external knowledge thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology |
| url | ONIX_20210501_9783039369645_858 |