Emotions Classifier based on Facial Expressions

Abstract

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".

Authors 5

1. Gabriel Rubio Aachen

   Universidad de Costa Rica · Inform (Germany)

   Affiliation as printed

   Universidad de Costa Rica,Escuela de Ciencias de la Computación e Informática,San José,Costa Rica

   , Costa Rica

2. Gabriela Barrantes Aachen

   Universidad de Costa Rica · Inform (Germany)

   Affiliation as printed

   Universidad de Costa Rica,Escuela de Ciencias de la Computación e Informática,San José,Costa Rica

   , Costa Rica

3. Juan José Vargas Aachen

   Universidad de Costa Rica · Inform (Germany)

   Affiliation as printed

   Universidad de Costa Rica,Escuela de Ciencias de la Computación e Informática,San José,Costa Rica

   , Costa Rica

4. Adrián Lara Aachen

   Universidad de Costa Rica · Inform (Germany)

   Affiliation as printed

   Universidad de Costa Rica,Escuela de Ciencias de la Computación e Informática,San José,Costa Rica

   , Costa Rica

5. Luis Quesada Aachen

   Universidad de Costa Rica · Inform (Germany)

   Affiliation as printed

   Universidad de Costa Rica,Escuela de Ciencias de la Computación e Informática,San José,Costa Rica

   , Costa Rica