This project aims to classify a group’s perceived emotion as Positive, Neutral or Negative. The dataset being used is the Group Affect Database 3.0 which contains "in the wild" photos of groups of people in various social environments.
Our solution is a hybrid machine learning system that builds on the model by Surace et al. and extends it further with additional and more refined machine learning methods and experiments. It has been published in the paper Group Emotion Recognition Using Machine Learning.
This repository consists of 3 branches -
master- contains the code used to train and test the model.webapp- contains the webapp.android- contains the android app.
- Fork this repository and clone the forked repository.
- Create and activate a Python 3 virtualenv.
- Use
pip install -r requirements.txtto install the requirements. - Download the final ensemble CNN model from here and model architecture from here and paste/replace in the cloned repository.
- Use
python classify_image.py image_dir original_labelto classify an image as Positive, Neutral or Negative. eg -python classify_image.py input/val/Positive/ Positiveclassifies the images in theinput/val/Positive/directory with original label asPositive.
- Fork this repository and clone the forked repository. (Ignore if already done in 1.1.)
- Use
git checkout webappto switch to the webapp branch. - Create and activate a Python 3 virtualenv. (Ignore if already done in 1.1.)
- Use
pip install -r requirements.txtto install the requirements. python manage.py runserverto start the server. Frontend can be accessed athttp://127.0.0.1:5000
So, first of all, why do we need emotion recognition?
Emotion recognition is important -
- To improve the user’s experience, as a customer, learner, or as a generic service user.
- Can help improve services without the need to formally and continuously ask the user for feedback.
- Also, using automatic emotion recognition in public safety, healthcare, or assistive technology, can significantly improve the quality of people’s lives, allowing them to live in a safer environment or reducing the impact that disabilities or other health conditions have.
Emotion Recognition has applications in crowd analytics, social media, marketing, event detection and summarization, public safety, human-computer interaction, digital security surveillance, street analytics, image retrieval, etc.
The problem of emotion recognition for a group of people has been less extensively studied, but it is gaining popularity due to the massive amount of data available on social networking sites containing images of groups of people participating in 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.
Our solution is a pipeline based approach which integrates two modules (that work in parallel): bottom-up and top-down modules, based on the idea that the emotion of a group of people can be deduced using both bottom-up and top-down approaches.
- The bottom-up module detects and extracts individual faces present in the image and passes them as input to an ensemble of pre-trained Deep Convolutional Neural Networks (CNNs).
- Simultaneously, the top-down module detects the labels associated with the scene and passes them as input to a Bayesian Network (BN) which predicts the probabilities of each class.
- In the final pipeline, the group emotion category predicted by 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.