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Project: face_classification (GitHub Link)

• face_classification-master
  • src
    • utils
      • visualizer.py
      • preprocessor.py
      • datasets.py
      • __init__.py
      • inference.py
      • data_augmentation.py
      • grad_cam.py
    • video_emotion_color_demo.py
    • web
      • faces.py
      • __init__.py
      • emotion_gender_processor.py
    • image_gradcam_demo.py
    • models
      • cnn.py
      • __init__.py
    • video_emotion_gender_demo.py
    • video_gradcam_demo.py
    • train_emotion_classifier.py
    • train_gender_classifier.py
    • image_emotion_gender_demo.py
    • __init__.py
  • datasets
    • .gitignore
  • images
  • trained_models
    • gender_models
    • detection_models
    • emotion_models
      • KDEF_emotion_training.log
  • LICENSE
  • REQUIREMENTS.txt
  • README.md
  • Dockerfile
  • .gitignore

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

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-classifier
• docker run -d -p 8084:8084 --name=face-classifier ekholabs/face-classifier
• curl -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).

• 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