Extreme Learning Machine Autoencoder for Data Augmentation.

Some databases used in computer vision problems have a few number of data points. It makes harder to the classifier to increase its generalization capability. One strategy applied to solve this issue is data augmentation. This solution aims to generate more data to improve the performance of the classifier. In this paper, we propose a model that uses images produced by an Extreme Learning Machine Autoencoder (ELM-AE) to make data augmentation. We selected the autoencoder approach since it is more straightforward and efficient than other data augmentation strategies. We evaluate our proposal in a facial expression recognition problem using the Japanese Female Facial Expression (JAFFE) database, and we assess the impact of the data augmentation in the performance considering K-Nearest Neighbors (KNN), Support Vector Machines (SVM) and Convolutional Neural Network (CNN). The obtained results show that our approach is an appropriate alternative for data augmentation tasks, also reaching better results when compared to other common strategies in most of the cases.