On the optimization of convolutional neural networks and its application for Face-based emotion recognition

Abstract

Deep learning techniques have been widely used in the last years due to their promising results in several applications, mainly face and object detection. However, one of their main shortcomings is related to the selection of suitable parameters in order to allow reasonable results. Since we may have millions of parameters, a manual fine tuning of them seems to be impractical. In this research proposal, we deal with this problem by means of meta-heuristic-based techniques, specifically the Migration Birds Optimization algorithm, since it has never been applied to the context of this project so far. Despite we can face several deep learning-based techniques out there, we opted to study Convolutional Neural Networks due to their high ability in learning good data representations. Additionally, the proposed work is validated in the context of face-based emotion recognition, as stated in the student's main project. (AU)