Méthodes d’ensemble et apprentissage profond pour l’analyse faciale en environnement non contraint
Estèphe Arnaud · theses.fr (ABES) · 2021
Despite the recent advances in face analysis, it is still difficult to adapt the models to the immense variety of morphological traits, head poses or occlusions that can strongly affect the face appearance. Thus, the objective of this thesis is to improve the robustness to these variations. To this end, we propose to combine deep learning methods with ensemble methods. In particular, we have proposed a new gating mechanism that allows to adaptively combine the decisions of an ensemble of predictors. This first architecture has been extended to allow the combination of an ensemble of representations. We also showed that it was possible to better guide this combination by using an exogenous variable, identified as an important source of variation in the face appearance. In addition to these architectural contributions, we have shown the contribution of a new training loss that improves the representation extracted by the network. The genericity and performance of the method have been experimentally validated. In particular, this method outperforms the state-of-the-art in both face alignment and facial expression recognition.