Real-time deep hair matting on mobile devices

Augmented reality is an emerging technology in many application domains. Among them is the beauty industry, where live virtual try-on of beauty products is of great importance. In this paper, we address the problem of live hair color augmentation. To achieve this goal, hair needs to be segmented quickly and accurately. We show how a modified MobileNet CNN architecture can be used to segment the hair in real-time. Instead of training this network using large amounts of accurate segmentation data, which is difficult to obtain, we use crowd sourced hair segmentation data. While such data is much simpler to obtain, the segmentations there are noisy and coarse. Despite this, we show how our system can produce accurate and fine-detailed hair mattes, while running at over 30 fps on an iPad Pro tablet.Comment: 7 pages, 7 figures, submitted to CRV 201

Hair Segmentation using Heuristically-trained Neural Networks

We present a method for binary classification using neural networks that performs training and classification on the same data using the help of a pre-training heuristic classifier. The heuristic classifier is initially used to segment data into three clusters of high confidence positives, high confidence negatives, and low confidence sets. The high confidence sets are used to train a neural network (NN) which is then used to classify the low confidence set. Applying this method to the binary classification of hair vs. non-hair patches, we obtain a 9% performance increase using the heuristically-trained NN over the current state of the art hair segmentation method.M.A.S