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

• URL: http://arxiv.org/abs/2204.12624v1
• Date: Tue, 26 Apr 2022 22:48:15 GMT
• Title: Evaluation of Self-taught Learning-based Representations for Facial Emotion Recognition
• Authors: Bruna Delazeri, Leonardo L. Veras, Alceu de S. Britto Jr., Jean Paul Barddal, Alessandro L. Koerich
• Abstract summary: This work describes different strategies to generate unsupervised representations obtained through the concept of self-taught learning for facial emotion recognition. The idea is to create complementary representations promoting diversity by varying the autoencoders' initialization, architecture, and training data. 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.
• Score: 62.30451764345482
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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