Paper ID: 2204.12624

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

Bruna Delazeri, Leonardo L. Veras, Alceu de S. Britto, Jean Paul Barddal, Alessandro L. Koerich

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.

Submitted: Apr 26, 2022