Two-stage human hair segmentation in the wild using deep shape prior

Abstract

Human hair is a crucial biometric characteristic with rich color and texture information. In this paper, we propose a novel hair segmentation approach integrating a deep shape prior into a carefully designed two-stage Fully Convolutional Neural Network (FCNN) pipeline. First, we utilize a FCNN with an Atrous Spatial Pyramid Pooling (ASPP) module to train a human hair shape prior based on a specific distance transform. In the second stage, we combine the hair shape prior and the original image to form the input of a symmetric encoder-decoder FCNN with a border refinement module to get the final hair segmentation output. Both quantitative and qualitative results show that our method achieves state-of-the-art performance on the LFW-Part and Figaro1k datasets.