@inproceedings{51b2f8f00679419683f551f18b3d85bb,
title = "Gramatical facial expression recognition with artificial intelligence tools",
abstract = "The face is the reflection of our emotions. We can guess the state of mind of a person by observing the face. In this paper, we applied an Associative Model algorithm to recognized Grammatical Facial Expressions. We used the dataset of the Brazilian sign language (Libras) system. The model we applied was a Morphological Associative Memory. We implemented a memory for each expression. The average of recognition for the same expression was of 98.89%. When we compare one expression with the others, we obtained a 98.59%, which means that our proposal confuses few expressions.",
keywords = "Associative memories, Computational intelligence, Facial expression, Pattern recognition",
author = "Elena Acevedo and Antonio Acevedo and Federico Felipe",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; Computing Conference, 2018 ; Conference date: 10-07-2018 Through 12-07-2018",
year = "2019",
doi = "10.1007/978-3-030-01174-1_45",
language = "Ingl{\'e}s",
isbn = "9783030011734",
series = "Advances in Intelligent Systems and Computing",
publisher = "Springer Verlag",
pages = "592--605",
editor = "Kohei Arai and Supriya Kapoor and Rahul Bhatia",
booktitle = "Intelligent Computing - Proceedings of the 2018 Computing Conference",
address = "Alemania",
}