Evaluation of Self-taught Learning-based Representations for Facial Emotion Recognition
Computer Vision and Pattern Recognition
2022-04-28 v1 Machine Learning
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.
Cite
@article{arxiv.2204.12624,
title = {Evaluation of Self-taught Learning-based Representations for Facial Emotion Recognition},
author = {Bruna Delazeri and Leonardo L. Veras and Alceu de S. Britto and Jean Paul Barddal and Alessandro L. Koerich},
journal= {arXiv preprint arXiv:2204.12624},
year = {2022}
}
Comments
8 pages