Hyperface

Hyperface Architecture and train pipeline for Hyperface paper in Keras with Tensorflow backend For now only AlexNet based architecture is implemented.

Train on sample data

1. Change the sample data path in config.py. The data format should be numpy serializable and have the following format For example, the sample data will be a numpy file. It will be a shape of ( X, 6 ) where X is the number of data points. The content of each element will be

   1. Image (numpy array)
   2. List which has [ 1, 1 ] if the given image is face or [ 0, 0 ] if the given image is non-face
   3. List of normalized facial coordinates [ 0.2536, 0.7890, …….. ]
   4. Visibility array [ 0, 0, 0, 1, 1, 1 …... ]
   5. Pose array [ 0.24304312, -0.42484489, -0.04113968 ]
   6. Gender array- [ 1, 0 ] for male and [ 0, 1 ] for female

2. Change other parameters for training in config.py.

3. Start the training with

   $ python train.py

Pakages required

• Python 2.7 or Python3.7 (tested on both)
• tensorflow
• keras
• numpy
• Opencv (for image resizing)