https://github.com/mhw32/face-to-emotion
Deep Learning Model to Predict Emotion Probabilities from Face-Cropped Images
https://github.com/mhw32/face-to-emotion
deep-learning emotion face-recognition flask
Last synced: 2 months ago
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Deep Learning Model to Predict Emotion Probabilities from Face-Cropped Images
- Host: GitHub
- URL: https://github.com/mhw32/face-to-emotion
- Owner: mhw32
- License: mit
- Created: 2017-08-19T19:23:56.000Z (almost 9 years ago)
- Default Branch: master
- Last Pushed: 2017-08-29T20:47:44.000Z (almost 9 years ago)
- Last Synced: 2025-02-05T04:33:00.035Z (over 1 year ago)
- Topics: deep-learning, emotion, face-recognition, flask
- Language: Python
- Homepage:
- Size: 1.08 MB
- Stars: 1
- Watchers: 4
- Forks: 1
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Face2Emotion
Deep convolutional net to predict coarse emotions from image frames.
The architecture is modeled after the technical report **Real-time Convolutional Neural Networks for Emotion and Gender Classification** by Octavio Arriaga and Paul G. Ploger.
The authors designed a Xception-based model that combines residual blocks (as in ResNet [[1](https://arxiv.org/abs/1512.03385)]) and depth-wise separable convolutions (as in the traditional Xception [[2](https://arxiv.org/abs/1611.05431)]).
The model is trained on the FER2013 dataset [[3](https://arxiv.org/abs/1307.0414)] and reaches 66% accuracy. See `models/` for Keras code and see `frozen` for trained weights.
## Flask Application
There's also a folder in `app/` that stores a vanilla web application with a single endpoint that reads a base64 encoded string representing an image and outputs a map of emotion probabilities per face.
**Note**: This assumes that incoming requests will also pass coordinates for facial objects in the scene. In our instance, we used the [True Face API](http://trueface.ai/). We use the coordinates to pre-crop our image before sending it to our model.
## Additional Info
This project was entered in the TrueFace.ai hackathon (1st place winner). A Heroku instance is currently up at [https://face2emotionapp.herokuapp.com/](https://face2emotionapp.herokuapp.com/).