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GrinAI

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Project Details

GrinAI is a full deep learning pipeline that classifies grins on a face. This model is implemented in a Twitter Bot, which tweets jokes if a person is not grinning, and a realtime camera, which can be run locally and notifies the user if they are not grinning.

Hackathon

This project was submitted to HackSTL 2019 in August 2019, which was a hackathon hosted at the Schamburg Township Library for 2 days or 21 hours.

Technologies

  • Python
  • Node.js
  • Keras
  • OpenCV

How We Built It

In order to develop the AI component, we chose to use Python and Keras. First, we found a public dataset of labeled mouths from a University. After applying preprocessing, we trained a Convolutional Neural Network to classify grins on the dataset. We experimented with different architectures and hyperparameters until we found a network with an accuracy of 91%. After this, we used this model and a Haar Cascade in a program that takes input from a computer camera and detects grins in real-time. In this program, the user has access to accuracy percentages and other controls.

Taking this one step further, we created a Twitter Bot using Node.js. After a user tweets a picture of themself, the picture is pulled from Twitter using the Twitter API and processed by our server. After the server recognizes a grin or no grin, the server tweets a response. If the person was grinning, then the server sends a message that says stay grinning. Otherwise, the server pulls jokes from a Joke API and tweets a random joke.

Overall, we were able to prove the concept of using Deep Learning and a Convolutional Neural Network to recognize grins. In addition, we implemented this concept in two unique ways.

Authors

Contributing

We encourage people to contribute to our website and suggest changes. Please create a pull request and email us with your suggestion.

For major changes, please open an issue first to discuss what you would like to change.

License

GNU General Public License v3.0

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GRIN AI - classifies grins on a human face

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