Face classification and detection.
Real-time face detection and emotion/gender classification using fer2013/IMDB datasets with a keras CNN model and openCV.
- IMDB gender classification test accuracy: 96%.
- fer2013 emotion classification test accuracy: 66%.
For more information please consult the publication
Emotion/gender examples:
Guided back-prop
Real-time demo:
B-IT-BOTS robotics team :)
Instructions
Run real-time emotion demo:
python3 video_emotion_color_demo.py
Run real-time guided back-prop demo:
python3 image_gradcam_demo.py
Make inference on single images:
python3 image_emotion_gender_demo.py <image_path>
e.g.
python3 image_emotion_gender_demo.py ../images/test_image.jpg
Running with Docker
With a few steps one can get its own face classification and detection running. Follow the commands below:
docker pull ekholabs/face-classifierdocker run -d -p 8084:8084 --name=face-classifier ekholabs/face-classifiercurl -v -F image=@[path_to_image] http://localhost:8084/classifyImage > image.png
To train previous/new models for emotion classification:
-
Download the fer2013.tar.gz file from here
-
Move the downloaded file to the datasets directory inside this repository.
-
Untar the file:
tar -xzf fer2013.tar
- Run the train_emotion_classification.py file
python3 train_emotion_classifier.py
To train previous/new models for gender classification:
-
Download the imdb_crop.tar file from here (It's the 7GB button with the tittle Download faces only).
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Move the downloaded file to the datasets directory inside this repository.
-
Untar the file:
tar -xfv imdb_crop.tar
- Run the train_gender_classification.py file
python3 train_gender_classifier.py