Abstract
Facial expressions are always manifested by people. For the human being, it is easy to recognize emotions. Technological advances applied to the recognition of expressions are growing rapidly and, in the same way, interest in research on this topic. However, at the computational level, it is a complicated task, some of the expressions of human beings are similar, for this reason, the computer can be confused at the moment of recognition. The use of machine learning models specifically artificial neural networks that have a good performance in emotions recognition is required to detect automatically feelings. This research shows the performance analysis of artificial neural networks applied to emotion datasets. The FER2013 and JAFFE datasets were used, a preprocessing of the data was carried out. For the classification, a comparison was made between artificial neural networks (Perceptron, VGG, and a Convolutional Neural Network). Optimal results were obtained in the detection of emotions.
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Notes
- 1.
Facial Expression Recognition 2013 https://datarepository.wolframcloud.com/resources/FER-2013.
- 2.
Japanese Female Facial Expression https://zenodo.org/record/3451524#.YP9_P-hKhPY.
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Lázaro-Lázaro, JJI., Sánchez-DelaCruz, E., Loeza-Mejía, CI., Pozos-Parra, P., Landero-Hernández, LA. (2021). Performance Evaluation of Artificial Neural Networks Applied in the Classification of Emotions. In: Batyrshin, I., Gelbukh, A., Sidorov, G. (eds) Advances in Computational Intelligence. MICAI 2021. Lecture Notes in Computer Science(), vol 13067. Springer, Cham. https://doi.org/10.1007/978-3-030-89817-5_17
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