A Simple Geometric-Based Descriptor for Facial Expression 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 propose a descriptor based on areas and angles of triangles formed by the landmarks from face images. We test these descriptors for...

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Guardado en:
Detalles Bibliográficos
Publicado: 2017
Materias:
Gesture recognition
Nearest neighbor search
Random processes
Conditional random field
Dynamic approaches
Facial expression recognition
Facial Expressions
K-nearest neighbors classifiers
Non-verbal human
Sets of features
Training example
Face recognition
Acceso en línea:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_97815090_v_n_p802_Acevedo
http://hdl.handle.net/20.500.12110/paper_97815090_v_n_p802_Acevedo
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id paper:paper_97815090_v_n_p802_Acevedo
record_format dspace
spelling paper:paper_97815090_v_n_p802_Acevedo2023-06-08T16:37:54Z A Simple Geometric-Based Descriptor for Facial Expression Recognition Gesture recognition Nearest neighbor search Random processes Conditional random field Dynamic approaches Facial expression recognition Facial Expressions K-nearest neighbors classifiers Non-verbal human Sets of features Training example 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 propose a descriptor based on areas and angles of triangles formed by the landmarks from face images. We test these descriptors for facial expression recognition by means of two different approaches. One is a dynamic approach where recognition is performed by a Conditional Random Field (CRF) classifier. The other approach is an adaptation of the k-Nearest Neighbors classifier called Citation-kNN in which the training examples come in the form of sets of feature vectors. An analysis of the most discriminative landmarks for the CRF approach is presented. We compare both methodologies, analyse their similarities and differences. Comparisons with other state-ofthe- art techniques on the CK+ dataset are shown. Even though both methodologies are different from each other, the descriptor remains robust and precise in the recognition of expressions. © 2017 IEEE. 2017 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_97815090_v_n_p802_Acevedo http://hdl.handle.net/20.500.12110/paper_97815090_v_n_p802_Acevedo
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic Gesture recognition
Nearest neighbor search
Random processes
Conditional random field
Dynamic approaches
Facial expression recognition
Facial Expressions
K-nearest neighbors classifiers
Non-verbal human
Sets of features
Training example
Face recognition
spellingShingle Gesture recognition
Nearest neighbor search
Random processes
Conditional random field
Dynamic approaches
Facial expression recognition
Facial Expressions
K-nearest neighbors classifiers
Non-verbal human
Sets of features
Training example
Face recognition
A Simple Geometric-Based Descriptor for Facial Expression Recognition
topic_facet Gesture recognition
Nearest neighbor search
Random processes
Conditional random field
Dynamic approaches
Facial expression recognition
Facial Expressions
K-nearest neighbors classifiers
Non-verbal human
Sets of features
Training example
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 propose a descriptor based on areas and angles of triangles formed by the landmarks from face images. We test these descriptors for facial expression recognition by means of two different approaches. One is a dynamic approach where recognition is performed by a Conditional Random Field (CRF) classifier. The other approach is an adaptation of the k-Nearest Neighbors classifier called Citation-kNN in which the training examples come in the form of sets of feature vectors. An analysis of the most discriminative landmarks for the CRF approach is presented. We compare both methodologies, analyse their similarities and differences. Comparisons with other state-ofthe- art techniques on the CK+ dataset are shown. Even though both methodologies are different from each other, the descriptor remains robust and precise in the recognition of expressions. © 2017 IEEE.
title A Simple Geometric-Based Descriptor for Facial Expression Recognition
title_short A Simple Geometric-Based Descriptor for Facial Expression Recognition
title_full A Simple Geometric-Based Descriptor for Facial Expression Recognition
title_fullStr A Simple Geometric-Based Descriptor for Facial Expression Recognition
title_full_unstemmed A Simple Geometric-Based Descriptor for Facial Expression Recognition
title_sort simple geometric-based descriptor for facial expression recognition
publishDate 2017
url https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_97815090_v_n_p802_Acevedo
http://hdl.handle.net/20.500.12110/paper_97815090_v_n_p802_Acevedo
_version_ 1768542481080647680

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