Deep Learning for Facial Informatics

• Autore: Hsu Gee-Sern Jison
• Pubblicazione: Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020
• Descrizione fisica: 1 electronic resource (102 p.)
• Soggetto topico: History of engineering & technology
• Soggetto non controllato: 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
• Persona (resp. second.): TimofteRadu; HsuGee-Sern Jison
• Sommario/riassunto: 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.
• Titolo autorizzato: Deep Learning for Facial Informatics
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910557721303321
• Lo trovi qui: Univ. Federico II