  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.
