Deep Multi-Facial Patches Aggregation Network For Facial Expression Recognition

TL;DR

This paper introduces a deep multi-facial patches aggregation network for facial expression recognition, utilizing data augmentation to improve performance on small datasets and achieving state-of-the-art results within the same dataset.

Contribution

The paper presents a novel deep architecture that aggregates features from facial patches and introduces two data augmentation techniques to enhance FER performance on limited datasets.

Findings

Achieves state-of-the-art results on three FER datasets.

Data augmentation improves recognition accuracy on small datasets.

Model performance drops significantly with dataset bias.

Abstract

In this paper, we propose an approach for Facial Expressions Recognition (FER) based on a deep multi-facial patches aggregation network. Deep features are learned from facial patches using deep sub-networks and aggregated within one deep architecture for expression classification . Several problems may affect the performance of deep-learning based FER approaches, in particular, the small size of existing FER datasets which might not be sufficient to train large deep learning networks. Moreover, it is extremely time-consuming to collect and annotate a large number of facial images. To account for this, we propose two data augmentation techniques for facial expression generation to expand FER labeled training datasets. We evaluate the proposed framework on three FER datasets. Results show that the proposed approach achieves state-of-art FER deep learning approaches performance when the model is trained and tested on images from the same dataset. Moreover, the proposed data augmentation techniques improve the expression recognition rate, and thus can be a solution for training deep learning FER models using small datasets. The accuracy degrades significantly when testing for dataset bias.