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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Detalles Bibliográficos
Autores principales: Iglesias, F., Negri, P., Buemi, M.E., Acevedo, D., Mejail, M.
Formato: CONF
Materias:
Conditional random field
Facial expressions classification
ORB descriptor
Pattern recognition
Pattern recognition systems
Random processes
Descriptors
Expression recognition
Facial expression recognition
Facial expressions classifications
Oriented fast and rotated brief (ORB)
Static and dynamic approach
Texture information
Face recognition
Acceso en línea:http://hdl.handle.net/20.500.12110/paper_NIS16339_v2016_n2_p_Iglesias
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id todo:paper_NIS16339_v2016_n2_p_Iglesias
record_format dspace
spelling todo:paper_NIS16339_v2016_n2_p_Iglesias2023-10-03T16:45:55Z Facial expression recognition: A comparison between static and dynamic approaches Iglesias, F. Negri, P. Buemi, M.E. Acevedo, D. Mejail, M. 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. CONF info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar 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
Iglesias, F.
Negri, P.
Buemi, M.E.
Acevedo, D.
Mejail, M.
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.
format CONF
author Iglesias, F.
Negri, P.
Buemi, M.E.
Acevedo, D.
Mejail, M.
author_facet Iglesias, F.
Negri, P.
Buemi, M.E.
Acevedo, D.
Mejail, M.
author_sort Iglesias, F.
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
url http://hdl.handle.net/20.500.12110/paper_NIS16339_v2016_n2_p_Iglesias
work_keys_str_mv AT iglesiasf facialexpressionrecognitionacomparisonbetweenstaticanddynamicapproaches
AT negrip facialexpressionrecognitionacomparisonbetweenstaticanddynamicapproaches
AT buemime facialexpressionrecognitionacomparisonbetweenstaticanddynamicapproaches
AT acevedod facialexpressionrecognitionacomparisonbetweenstaticanddynamicapproaches
AT mejailm facialexpressionrecognitionacomparisonbetweenstaticanddynamicapproaches
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