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arXiv · 2204.12624

Evaluation of Self-taught Learning-based Representations for Facial Emotion Recognition

Bruna Delazeri·Leonardo L. Veras·Alceu de S. Britto Jr.·Jean Paul Barddal·Alessandro L. Koerich
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Abstract

This work describes different strategies to generate unsupervised representations obtained through the concept of self-taught learning for facial emotion recognition (FER). The idea is to create complementary representations promoting diversity by varying the autoencoders' initialization, architecture, and training data. SVM, Bagging, Random Forest, and a dynamic ensemble selection method are evaluated as final classification methods. Experimental results on Jaffe and Cohn-Kanade datasets using a leave-one-subject-out protocol show that FER methods based on the proposed diverse representations compare favorably against state-of-the-art approaches that also explore unsupervised feature learning.

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Bruna Delazeri, Leonardo L. Veras, Alceu de S. Britto Jr., Jean Paul Barddal, Alessandro L. Koerich. 2022-04-26. Evaluation of Self-taught Learning-based Representations for Facial Emotion Recognition. https://arxiv.org/abs/2204.12624

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