Facial

github
https://github.com/chinakook/hr101_mxnet and

tiny faces
https://github.com/cydonia999/Tiny_Faces_in_Tensorflow Python and tensorflow, optimized for rapid facial detection.

faceboxes
https://github.com/zisianw/FaceBoxes.PyTorch Although tremendous strides have been made in face detection, one of the remaining open challenges is to achieve real-time speed on the CPU as well as maintain high performance, since effective models for face detection tend to be computationally prohibitive. To address this challenge, we propose a novel face detector, named FaceBoxes, with superior performance on both speed and accuracy. Specifically, our method has a lightweight yet powerful network structure that consists of the Rapidly Digested Convolutional Layers (RDCL) and the Multiple Scale Convolutional Layers (MSCL).

ageitgey
https://github.com/ageitgey/face_recognition 21000 stars

face obfuscation
https://github.com/Yijunmaverick/GenerativeFaceCompletion uses https://github.com/RadekSimkanic/caffe-for-cudnn-v2.5.48

https://arxiv.org/abs/1704.05838 In this paper, we propose an effective face completion algorithm using a deep generative model. Different from well-studied background completion, the face completion task is more challenging as it often requires to generate semantically new pixels for the missing key components (e.g., eyes and mouths) that contain large appearance variations. Unlike existing nonparametric algorithms that search for patches to synthesize, our algorithm directly generates contents for missing regions based on a neural network. The model is trained with a combination of a reconstruction loss, two adversarial losses and a semantic parsing loss, which ensures pixel faithfulness and local-global contents consistency. With extensive experimental results, we demonstrate qualitatively and quantitatively that our model is able to deal with a large area of missing pixels in arbitrary shapes and generate realistic face completion results.

links
Object tracking of faces.