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HyperFace

Contains the implementation of HyperFace: A deep multi task learning framework for facial recognition, landmark detection, pose and gender detection

TODO :

  • Implement HyperFace-AlexNet

    • Selective search
    • Iterative region proposal (IRP)
    • Landmark based - Non Maximum Suppression (L-NMS)
    • skip training RCNN on imagenet and directly train on region proposals from AFLW dataset
    • Copy weights to the hyperface model and retrain it on AFLW dataset
  • Implement HyperFace-ResNet101

    • Uses keras.application to load model weights and retrain R-CNN
    • Re-write the model architecture for resnet and carry out training
  • Experimental stuff

    • Find better loss functions/ Implement custom loss functions
    • Find better optimizer
    • Decide on using ReduceLROnPlateau or not, look for useful callbacks
  • Future Plans

    • Implement using ResNet
    • Divide the code into seperate files
    • support command line arguments
  • Known issues

    • openCV's imread function reads in BGR instead of RGB, skimage ioread is comparatively slower than openCV, matplotlib shows wrong image plots because of BGR mode
    • Some images are bw by default so proper dimension conversion can not be done at all images (at the moment those images are skipped to save me from headache)
    • Processing all images uses up 14gb+ of RAM, so try and implement some other way because I plan on implementing data augementation in future to increase face detection accuracy
    • No official keras implementation of ResNet101, this code relies on experimental code(ro be changed in next version update)

Current model archietecture (AlexNet) is shown below -

HyperFace AlexNet