Contains the implementation of HyperFace: A deep multi task learning framework for facial recognition, landmark detection, pose and gender detection
TODO :
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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
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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
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Experimental stuff
Find better loss functions/ Implement custom loss functionsFind better optimizerDecide on using ReduceLROnPlateau or not, look for useful callbacks
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Future Plans
- Implement using ResNet
Divide the code into seperate files- support command line arguments
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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)
