InFER: A Multi-Ethnic Indian Facial Expression Recognition Dataset

Syed Rizvi, Preyansh Agrawal, Jagat Challa, Pratik Narang

2023

Abstract

The rapid advancement in deep learning over the past decade has transformed Facial Expression Recognition (FER) systems, as newer methods have been proposed that outperform the existing traditional handcrafted techniques. However, such a supervised learning approach requires a sufficiently large training dataset covering all the possible scenarios. And since most people exhibit facial expressions based upon their age group, gender, and ethnicity, a diverse facial expression dataset is needed. This becomes even more crucial while developing a FER system for the Indian subcontinent, which comprises of a diverse multi-ethnic population. In this work, we present InFER, a real-world multi-ethnic Indian Facial Expression Recognition dataset consisting of 10,200 images and 4,200 short videos of seven basic facial expressions. The dataset has posed expressions of 600 human subjects, and spontaneous/acted expressions of 6000 images crowd-sourced from the internet. To the best of our knowledge InFER is the first of its kind consisting of images from 600 subjects from very diverse ethnicity of the Indian Subcontinent. We also present the experimental results of baseline & deep FER methods on our dataset to substantiate its usability in real-world practical applications.

Paper Citation

in Harvard Style

Rizvi S., Agrawal P., Challa J. and Narang P. (2023). InFER: A Multi-Ethnic Indian Facial Expression Recognition Dataset. In Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, ISBN 978-989-758-623-1, pages 550-557. DOI: 10.5220/0011699400003393

in Bibtex Style

@conference{icaart23,
author={Syed Rizvi and Preyansh Agrawal and Jagat Challa and Pratik Narang},
title={InFER: A Multi-Ethnic Indian Facial Expression Recognition Dataset},
booktitle={Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,},
year={2023},
pages={550-557},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011699400003393},
isbn={978-989-758-623-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,
TI - InFER: A Multi-Ethnic Indian Facial Expression Recognition Dataset
SN - 978-989-758-623-1
AU - Rizvi S.
AU - Agrawal P.
AU - Challa J.
AU - Narang P.
PY - 2023
SP - 550
EP - 557
DO - 10.5220/0011699400003393