Deep Learning for Spatio-Temporal Modeling of Dynamic Spontaneous Emotions
| Autor: | Dawood Al Chanti, Alice Caplier |
|---|---|
| Přispěvatelé: | GIPSA - Architecture, Géométrie, Perception, Images, Gestes (GIPSA-AGPIG), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]) |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
3D-CNN
SPP-net Computer science Context (language use) 02 engineering and technology 010501 environmental sciences 01 natural sciences Facial recognition system [PHYS.ASTR.CO]Physics [physics]/Astrophysics [astro-ph]/Cosmology and Extra-Galactic Astrophysics [astro-ph.CO] Deep Learning [STAT.ML]Statistics [stat]/Machine Learning [stat.ML] ConvLSTM 0202 electrical engineering electronic engineering information engineering Spatiotemporal Features Layer (object-oriented design) 0105 earth and related environmental sciences Facial expression Artificial neural network business.industry Deep learning [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] Pattern recognition Expression (mathematics) Human-Computer Interaction Facial Expression Dynamic Emotion Face (geometry) [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] 020201 artificial intelligence & image processing Artificial intelligence business Software |
| Zdroj: | IEEE Transactions on Affective Computing IEEE Transactions on Affective Computing, Institute of Electrical and Electronics Engineers, 2018, pp.1-1. ⟨10.1109/TAFFC.2018.2873600⟩ |
| ISSN: | 1949-3045 |
| DOI: | 10.1109/TAFFC.2018.2873600⟩ |
| Popis: | International audience; Facial expressions involve dynamic morphological changes in a face, conveying information about the expresser's feelings. Each emotion has a specific spatial deformation over the face and temporal profile with distinct time segments. We aim at modeling the human dynamic emotional behavior by taking into consideration the visual content of the face and its evolution. But emotions can both speed-up or slow-down, therefore it is important to incorporate information from the local neighborhood frames (short-term dependencies) and the global setting (long-term dependencies) to summarize the segment context despite of its time variations. A 3D-Convolutional Neural Networks (3D-CNN) is used to learn early local spatiotemporal features. The 3D-CNN is designed to capture subtle spatiotemporal changes that may occur on the face. Then a Convolutional-Long-Short-Term-Memory (ConvLSTM) network is designed to learn semantic information by taking into account longer spatiotemporal dependencies. The ConvLSTM network helps considering the global visual saliency of the expression. That is locating and learning features in space and time that stand out from their local neighbors in order to signify distinctive facial expression features along the entire sequence. Non-variant representations based on aggregating global spatiotemporal features at increasingly fine resolutions are then done using a weighted Spatial Pyramid Pooling layer. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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