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Journal Articles Pattern Recognition Letters Year : 2020

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

Yongzhe Yan
Xavier Naturel
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Thierry Chateau

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.
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Dates and versions

hal-02890593 , version 1 (06-07-2020)

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Yongzhe Yan, Stefan Duffner, Xavier Naturel, Anthony Berthelier, Christophe Garcia, et al.. Two-stage human hair segmentation in the wild using deep shape prior. Pattern Recognition Letters, 2020, 136, pp.293-300. ⟨10.1016/j.patrec.2020.06.014⟩. ⟨hal-02890593⟩
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