Group Emotion Recognition Using Machine Learning

05/03/2019
by   Samanyou Garg, et al.
0

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.

READ FULL TEXT
research
09/12/2017

Emotion Recognition in the Wild using Deep Neural Networks and Bayesian Classifiers

Group emotion recognition in the wild is a challenging problem, due to t...
research
07/09/2018

An Attention Model for group-level emotion recognition

In this paper we propose a new approach for classifying the global emoti...
research
05/08/2021

Facial Emotion Recognition: State of the Art Performance on FER2013

Facial emotion recognition (FER) is significant for human-computer inter...
research
10/02/2019

Automatic Group Cohesiveness Detection With Multi-modal Features

Group cohesiveness is a compelling and often studied composition in grou...
research
09/15/2020

Group-Level Emotion Recognition Using a Unimodal Privacy-Safe Non-Individual Approach

This article presents our unimodal privacy-safe and non-individual propo...
research
06/06/2020

Ensemble Network for Ranking Images Based on Visual Appeal

We propose a computational framework for ranking images (group photos in...
research
12/31/2018

Predicting Group Cohesiveness in Images

Cohesiveness of a group is an essential indicator of emotional state, st...

Please sign up or login with your details

Forgot password? Click here to reset