Margin-Mix: Semi-Supervised Learning for Face Expression Recognition

Corneliu Florea, Mihai Sorin Badea, Laura Florea, Andrei Racoviteanu, Constantin Vertan

Abstract:

In this paper, as we aim to construct a semi-supervised learning algorithm, we exploit the characteristics of the Deep Convolutional
Networks to provide, for an input image, both an embedding descriptor and a prediction. The unlabeled data is combined with the labeled
one in order to provide synthetic data, which describes better the input space. The network is asked to provide a large margin between clusters, while new data is self-labeled by the distance to class centroids, in the embedding space. The method is tested on standard benchmarks for semi–supervised learning, where it matches state of the art performance and on the problem of face expression recognition where it increases the accuracy by a noticeable margin.