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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.
Cited literature [50 references]
https://hal.archives-ouvertes.fr/hal-02890593
Contributor : Yongzhe Yan Connect in order to contact the contributor
Submitted on : Monday, July 6, 2020 - 2:16:09 PM
Last modification on : Thursday, September 9, 2021 - 2:36:02 PM
Long-term archiving on: : Monday, September 28, 2020 - 2:18:20 PM
Contributor : Yongzhe Yan Connect in order to contact the contributor
Submitted on : Monday, July 6, 2020 - 2:16:09 PM
Last modification on : Thursday, September 9, 2021 - 2:36:02 PM
Long-term archiving on: : Monday, September 28, 2020 - 2:18:20 PM
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