Spontaneous Facial Expression Recognition using Sparse Representation
| العنوان: | Spontaneous Facial Expression Recognition using Sparse Representation |
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| المؤلفون: | Dawood Al Chanti, Alice Caplier |
| المساهمون: | 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]) |
| المصدر: | VISIGRAPP 2017-12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications VISIGRAPP 2017-12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, Feb 2017, Porto, Portugal. pp.11, ⟨10.5220/0006118000640074⟩ VISIGRAPP (5: VISAPP) Scopus-Elsevier |
| بيانات النشر: | figshare, 2017. |
| سنة النشر: | 2017 |
| مصطلحات موضوعية: | FOS: Computer and information sciences, Computer science, Sparse Representation, Random projection, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Initialization, 02 engineering and technology, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Discriminative model, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], ComputerApplications_MISCELLANEOUS, 0202 electrical engineering, electronic engineering, information engineering, Feature (machine learning), Spontaneous Facial Expression, Random Projection, Linear combination, Facial expression, business.industry, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 020207 software engineering, Pattern recognition, Sparse approximation, Dictionary Learning, ComputingMethodologies_PATTERNRECOGNITION, Face (geometry), [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Artificial intelligence, business |
| الوصف: | Facial expression is the most natural means for human beings to communicate their emotions. Most facial expression analysis studies consider the case of acted expressions. Spontaneous facial expression recognition is significantly more challenging since each person has a different way to react to a given emotion. We consider the problem of recognizing spontaneous facial expression by learning discriminative dictionaries for sparse representation. Facial images are represented as a sparse linear combination of prototype atoms via Orthogonal Matching Pursuit algorithm. Sparse codes are then used to train an SVM classifier dedicated to the recognition task. The dictionary that sparsifies the facial images (feature points with the same class labels should have similar sparse codes) is crucial for robust classification. Learning sparsifying dictionaries heavily relies on the initialization process of the dictionary. To improve the performance of dictionaries, a random face feature descriptor based on the Random Projection concept is developed. The effectiveness of the proposed method is evaluated through several experiments on the spontaneous facial expressions DynEmo database. It is also estimated on the well-known acted facial expressions JAFFE database for a purpose of comparison with state-of-the-art methods. Comment: 11 pages, 9 figures, VISAPP 2017, publisher=SciTePress, organization=INSTICC, isbn=978-989-758-226-4, Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, (VISIGRAPP 2017)}, 2017 |
| DOI: | 10.6084/m9.figshare.5212414.v1 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::174fd94a226be2ec07f17ba9379d42a2Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....174fd94a226be2ec07f17ba9379d42a2 |
| قاعدة البيانات: | OpenAIRE |
| DOI: | 10.6084/m9.figshare.5212414.v1 |
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