Facial expression recognition using lightweight deep convolutional networks with label distribution learning on action units labels space

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Nowadays, the search for ‘lightweight’ solutions that achieve comparable results to those of heavy deep learning models has received increasing attention due to a feasible implementation on mobile devices. One of the areas that might benefit from this approach is the task of Facial Expression Recog...

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Detalles Bibliográficos
Autores principales: Mastropasqua, Nicolás, Acevedo, Daniel
Formato: Articulo
Lenguaje:Español
Publicado: 2023
Materias:
Ciencias Informáticas
Facial expression recognition
Label distribution learning
Lightweight
Convolutional Neuronal Network
Action unit recognition
Reconocimiento de expresiones faciales
Aprendizaje de distribuciones de etiquetas
Redes convolucionales livianas
Reconocimiento de Action Units
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/157870
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-157870
record_format dspace
spelling I19-R120-10915-1578702023-09-20T20:04:26Z http://sedici.unlp.edu.ar/handle/10915/157870 Facial expression recognition using lightweight deep convolutional networks with label distribution learning on action units labels space Reconocimiento de expresiones faciales con redes profundas livianas usando Label Distribution Learning y el espacio de Action Units Mastropasqua, Nicolás Acevedo, Daniel 2023-06 2023-09-20T13:44:19Z es Ciencias Informáticas Facial expression recognition Label distribution learning Lightweight Convolutional Neuronal Network Action unit recognition Reconocimiento de expresiones faciales Aprendizaje de distribuciones de etiquetas Redes convolucionales livianas Reconocimiento de Action Units Nowadays, the search for ‘lightweight’ solutions that achieve comparable results to those of heavy deep learning models has received increasing attention due to a feasible implementation on mobile devices. One of the areas that might benefit from this approach is the task of Facial Expression Recognition (FER). Considering the fact that datasets usually come with categoric labeling but most emotions occur as combinations, mixtures, or compounds of the basic emotions, we make use of label distribution learning (LDL) as a training strategy. In this article we deal with the FER problem using lightweight neuronal networks and LDL. We further assume that facial images should have similar emotion distributions to their neighbors when the right auxiliary task is considered, like the Action Unit Recognition problem. This neighbors’ distribution information is captured in the loss function to help the LDL training process. Specifically, we conduct an analysis of EfficientFace, a state-of-the-art ligthweight CNN and we analyze the impact of using different approaches to LDL on a variety of in-the-wild datasets: RAF-DB, CAER-S, FER+ and AffectNet. Hoy en día, la búsqueda de soluciones lightweight que logren resultados comparables a modelos de Deep learning robustos ha recibido particular atención debido a su implementación factible en dispositivos móviles. Uno de los problemas que podrían aprovechar esta cualidad es el de Facial Expression Recognition (FER). Considerando que una gran cantidad de datasets de expresiones faciales suelen estar anotados con emociones categóricas cuando en realidad la mayoría de las expresiones exhibidas en escenarios ‘in the wild’ ocurren como combinaciones o composición de emociones básicas, se puede hacer uso de Label Distibution Learning (LDL) como estrategia para el entrenamiento. En este trabajo se abordará el problema de FER a través de redes neuronales livianas entrenadas con LDL. Bajo el supuesto de que las imágenes de expresiones faciales deberían tener una distribución de emoción similar a la de su vecindad en un espacio de etiquetas auxiliares adecuado, como aquel determinado por la tarea de Action Unit recognition, se puede aprovechar la información de las distribuciones e incorporarla como parte la función de pérdida. Concretamente, se estudiarán en profundidad la arquitectura lightweight EfficientFace y se analizará el impacto de distintos acercamientos para implementar LDL considerando datasets ‘in the wild’ como RAF-DB, CAER-S, FER+ y AffectNet. Sociedad Argentina de Informática e Investigación Operativa Articulo Articulo http://creativecommons.org/licenses/by-nc/4.0/ Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) application/pdf 170-195
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Español
topic Ciencias Informáticas
Facial expression recognition
Label distribution learning
Lightweight
Convolutional Neuronal Network
Action unit recognition
Reconocimiento de expresiones faciales
Aprendizaje de distribuciones de etiquetas
Redes convolucionales livianas
Reconocimiento de Action Units
spellingShingle Ciencias Informáticas
Facial expression recognition
Label distribution learning
Lightweight
Convolutional Neuronal Network
Action unit recognition
Reconocimiento de expresiones faciales
Aprendizaje de distribuciones de etiquetas
Redes convolucionales livianas
Reconocimiento de Action Units
Mastropasqua, Nicolás
Acevedo, Daniel
Facial expression recognition using lightweight deep convolutional networks with label distribution learning on action units labels space
topic_facet Ciencias Informáticas
Facial expression recognition
Label distribution learning
Lightweight
Convolutional Neuronal Network
Action unit recognition
Reconocimiento de expresiones faciales
Aprendizaje de distribuciones de etiquetas
Redes convolucionales livianas
Reconocimiento de Action Units
description Nowadays, the search for ‘lightweight’ solutions that achieve comparable results to those of heavy deep learning models has received increasing attention due to a feasible implementation on mobile devices. One of the areas that might benefit from this approach is the task of Facial Expression Recognition (FER). Considering the fact that datasets usually come with categoric labeling but most emotions occur as combinations, mixtures, or compounds of the basic emotions, we make use of label distribution learning (LDL) as a training strategy. In this article we deal with the FER problem using lightweight neuronal networks and LDL. We further assume that facial images should have similar emotion distributions to their neighbors when the right auxiliary task is considered, like the Action Unit Recognition problem. This neighbors’ distribution information is captured in the loss function to help the LDL training process. Specifically, we conduct an analysis of EfficientFace, a state-of-the-art ligthweight CNN and we analyze the impact of using different approaches to LDL on a variety of in-the-wild datasets: RAF-DB, CAER-S, FER+ and AffectNet.
format Articulo
Articulo
author Mastropasqua, Nicolás
Acevedo, Daniel
author_facet Mastropasqua, Nicolás
Acevedo, Daniel
author_sort Mastropasqua, Nicolás
title Facial expression recognition using lightweight deep convolutional networks with label distribution learning on action units labels space
title_short Facial expression recognition using lightweight deep convolutional networks with label distribution learning on action units labels space
title_full Facial expression recognition using lightweight deep convolutional networks with label distribution learning on action units labels space
title_fullStr Facial expression recognition using lightweight deep convolutional networks with label distribution learning on action units labels space
title_full_unstemmed Facial expression recognition using lightweight deep convolutional networks with label distribution learning on action units labels space
title_sort facial expression recognition using lightweight deep convolutional networks with label distribution learning on action units labels space
publishDate 2023
url http://sedici.unlp.edu.ar/handle/10915/157870
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AT mastropasquanicolas reconocimientodeexpresionesfacialesconredesprofundaslivianasusandolabeldistributionlearningyelespaciodeactionunits
AT acevedodaniel reconocimientodeexpresionesfacialesconredesprofundaslivianasusandolabeldistributionlearningyelespaciodeactionunits
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