Emotions Classifier based on Facial Expressions


Emotion recognition is important in the context of smart buildings and IoT, because it allows the environment to have a better notion of the mood of the humans who are present. With a view to developing such projects, in this article we analyze the performance of an emotion classifier that uses a convolutional neural network. Specifically, we focus on analyzing the impact of the epochs and batch size hyperparameters. To do this, we propose an experimental design with the following hypothesis: "The number of epochs that the model trains and the size of the batch given by iteration in each epoch influence the accuracy of an emotion classifier built from networks. convolutional neurons using the VGG16 architecture".

Tipo de publicación: Conference Paper

Publicado en: 2021 IEEE V Jornadas Costarricenses de Investigación en Computación e Informática (JoCICI)

  • Alejandro Benavides
  • Gabriel Rubio
  • Gabriela Barrantes
  • Juan Jose Vargas
  • Adrian Lara
  • Luis Quesada

Investigadores del CITIC asociados a la publicación
E. Gabriela Barrantes Sliesarieva
Juan José Vargas Morales
Adrian Lara Petitdemange
Luis Quesada Quirós

Proyecto asociado a la publicación


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Cita bibliográfica
Emotions Classifier based on Facial Expressions