Most facial expression recognition (FER) systems rely on machine learning approaches that require large databases (DBs) for effective training. As these are not easily available, a good solution is to augment the DBs with appropriate techniques, which are typically based on either geometric transformation or deep learning based technologies (e.g., Generative Adversarial Networks (GANs)). Whereas the first category of techniques has been fairly adopted in the past, studies that use GAN-based techniques are limited for FER systems. To advance in this respect, we evaluate the impact of the GAN techniques by creating a new DB containing the generated synthetic images. The face images contained in the KDEF DB serve as the basis for creating novel synthetic images by combining the facial features of two images selected from the YouTube-Faces DB.
The dataset is available on IEEE Dataport (needs IEEE membership subscription) and Zenodo (open-access).
The published article is available on MDPI Electronics (open-access).
If you have any questions or requests, please contact Simone Porcu or Alessandro Floris.
If you make use of this dataset, please consider citing the following publication:
Porcu, S., Floris, A., & Atzori, L. (2020). Evaluation of Data Augmentation Techniques for Facial Expression Recognition Systems. Electronics, 9, 1892, doi: 10.3390/electronics9111892.
BibTex format:
@article{porcu2020evaluation, title={Evaluation of Data Augmentation Techniques for Facial Expression Recognition Systems}, author={Porcu, Simone and Floris, Alessandro and Atzori, Luigi}, journal={Electronics}, volume={9}, pages={108781}, year={2020}, number = {11}, article-number = {1892}, publisher={MDPI}, doi={10.3390/electronics9111892} }