Semi-supervised Facial Expressions Annotation Using Co-Training with Fast Probabilistic Tri-Class SVMs

Abstract

Supervised learning requires a large amount of labeled data but the data labeling process can be expensive and time consuming, as it requires the efforts of human experts. Semi-supervised learning methods that can reduce the amount of required labeled data through exploiting the available unlabeled data to improve the classification accuracy. Here, we propose a learning framework to exploit the unlabeled data by decomposing multi-class problems into a set of binary problems and apply Co-Training to each binary problem. A probabilistic version of Tri-Class Support Vector Machine is proposed (SVM) that can discriminate between ignorance and uncertainty and an updated version of Sequential Minimal Optimization (SMO) algorithm is used for fast learning of Tri-Class SVMs. The proposed framework is applied to facial expressions recognition task. The results show that Co-Training can exploit effectively the independent views and the unlabeled data to improve the recognition accuracy of facial expressions.