Deep Multi-Facial Patches Aggregation Network For Facial Expression Recognition
This work addresses facial expression recognition for applications like human-computer interaction, but it is incremental as it builds on existing deep learning methods with specific enhancements.
The paper tackles facial expression recognition by proposing a deep multi-facial patches aggregation network and data augmentation techniques to address small dataset sizes, achieving state-of-the-art performance on same-dataset tests with improved recognition rates, though accuracy degrades under dataset bias.
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.