kumarnikhil936/facial_recognition_inception_network

This implementation is based on the Facenet paper published by Google, which proposes the idea of using inception module (basically inception network) for the task of facial recognition. This method uses a deep convolutional network trained to directly optimize the embedding itself, rather than an intermediate bottleneck layer as in previous deep learning approaches where the training is done on the complete picture rather than the face area only. To train, triplets of roughly aligned matching / non-matching face patches generated using a novel online triplet mining method. However, here we will be using the pretrained weights which is uploaded here as well for one's easy access. The benefit of this approach is much greater representational efficiency, since face recognition performance is using only 128-bytes per face.