  Automatic facial emotion recognition is a challenging task that has gained
significant scientific interest over the past few years, but the problem of
emotion recognition for a group of people has been less extensively studied.
However, it is slowly gaining popularity due to the massive amount of data
available on social networking sites containing images of groups of people
participating in various social events. Group emotion recognition is a
challenging problem due to obstructions like head and body pose variations,
occlusions, variable lighting conditions, variance of actors, varied indoor and
outdoor settings and image quality. The objective of this task is to classify a
group's perceived emotion as Positive, Neutral or Negative. In this report, we
describe our solution which is a hybrid machine learning system that
incorporates deep neural networks and Bayesian classifiers. Deep Convolutional
Neural Networks (CNNs) work from bottom to top, analysing facial expressions
expressed by individual faces extracted from the image. The Bayesian network
works from top to bottom, inferring the global emotion for the image, by
integrating the visual features of the contents of the image obtained through a
scene descriptor. In the final pipeline, the group emotion category predicted
by an ensemble of CNNs in the bottom-up module is passed as input to the
Bayesian Network in the top-down module and an overall prediction for the image
is obtained. Experimental results show that the stated system achieves 65.27%
accuracy on the validation set which is in line with state-of-the-art results.
As an outcome of this project, a Progressive Web Application and an
accompanying Android app with a simple and intuitive user interface are
presented, allowing users to test out the system with their own pictures.
