Facial expression recognition: A comparison between static and dynamic approaches
The identification of facial expressions with human emotions plays a key role in non-verbal human communication and has applications in several areas. In this work, we analyze two approaches for expression recognition. One of them is a staticbased appearance method. In this approach, a binary-based...
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paper:paper_NIS16339_v2016_n2_p_Iglesias2023-06-08T16:39:38Z Facial expression recognition: A comparison between static and dynamic approaches Buemi, María Elena Acevedo, Daniel G. Mejail, Marta Estela Conditional random field Facial expressions classification ORB descriptor Pattern recognition Pattern recognition systems Random processes Conditional random field Descriptors Expression recognition Facial expression recognition Facial expressions classifications Oriented fast and rotated brief (ORB) Static and dynamic approach Texture information Face recognition The identification of facial expressions with human emotions plays a key role in non-verbal human communication and has applications in several areas. In this work, we analyze two approaches for expression recognition. One of them is a staticbased appearance method. In this approach, a binary-based descriptor, denominated Oriented Fast and Rotated BRIEF (ORB), is used on a single frame of a sequence of images to extract texture information, and classified with a Support Vector Machine. The other is a dynamic approach introducing a new simple descriptor based on the angles formed by the landmarks to capture the dynamic of the gesture on an image sequence. In this case the recognition is performed by a Conditional Random Field (CRF) classifier. The paper compares both methodologies, analyze their similarities and differences. Fil:Buemi, M.E. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Acevedo, D. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Mejail, M. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. 2016 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_NIS16339_v2016_n2_p_Iglesias http://hdl.handle.net/20.500.12110/paper_NIS16339_v2016_n2_p_Iglesias |
institution |
Universidad de Buenos Aires |
institution_str |
I-28 |
repository_str |
R-134 |
collection |
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
topic |
Conditional random field Facial expressions classification ORB descriptor Pattern recognition Pattern recognition systems Random processes Conditional random field Descriptors Expression recognition Facial expression recognition Facial expressions classifications Oriented fast and rotated brief (ORB) Static and dynamic approach Texture information Face recognition |
spellingShingle |
Conditional random field Facial expressions classification ORB descriptor Pattern recognition Pattern recognition systems Random processes Conditional random field Descriptors Expression recognition Facial expression recognition Facial expressions classifications Oriented fast and rotated brief (ORB) Static and dynamic approach Texture information Face recognition Buemi, María Elena Acevedo, Daniel G. Mejail, Marta Estela Facial expression recognition: A comparison between static and dynamic approaches |
topic_facet |
Conditional random field Facial expressions classification ORB descriptor Pattern recognition Pattern recognition systems Random processes Conditional random field Descriptors Expression recognition Facial expression recognition Facial expressions classifications Oriented fast and rotated brief (ORB) Static and dynamic approach Texture information Face recognition |
description |
The identification of facial expressions with human emotions plays a key role in non-verbal human communication and has applications in several areas. In this work, we analyze two approaches for expression recognition. One of them is a staticbased appearance method. In this approach, a binary-based descriptor, denominated Oriented Fast and Rotated BRIEF (ORB), is used on a single frame of a sequence of images to extract texture information, and classified with a Support Vector Machine. The other is a dynamic approach introducing a new simple descriptor based on the angles formed by the landmarks to capture the dynamic of the gesture on an image sequence. In this case the recognition is performed by a Conditional Random Field (CRF) classifier. The paper compares both methodologies, analyze their similarities and differences. |
author |
Buemi, María Elena Acevedo, Daniel G. Mejail, Marta Estela |
author_facet |
Buemi, María Elena Acevedo, Daniel G. Mejail, Marta Estela |
author_sort |
Buemi, María Elena |
title |
Facial expression recognition: A comparison between static and dynamic approaches |
title_short |
Facial expression recognition: A comparison between static and dynamic approaches |
title_full |
Facial expression recognition: A comparison between static and dynamic approaches |
title_fullStr |
Facial expression recognition: A comparison between static and dynamic approaches |
title_full_unstemmed |
Facial expression recognition: A comparison between static and dynamic approaches |
title_sort |
facial expression recognition: a comparison between static and dynamic approaches |
publishDate |
2016 |
url |
https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_NIS16339_v2016_n2_p_Iglesias http://hdl.handle.net/20.500.12110/paper_NIS16339_v2016_n2_p_Iglesias |
work_keys_str_mv |
AT buemimariaelena facialexpressionrecognitionacomparisonbetweenstaticanddynamicapproaches AT acevedodanielg facialexpressionrecognitionacomparisonbetweenstaticanddynamicapproaches AT mejailmartaestela facialexpressionrecognitionacomparisonbetweenstaticanddynamicapproaches |
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1769175823540027392 |