  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.
