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.

人发是至关重要的生物特征，具有丰富的颜色和纹理信息。在本文中，我们提出了一种新颖的头发分割方法，该方法将先验的深层形状整合到精心设计的两阶段全卷积神经网络（FCNN）管道中。首先，我们将FCNN与Atrous空间金字塔池（ASPP）模块结合使用，以根据特定的距离变换预先训练人的头发形状。在第二阶段中，我们将先验的头发形状和原始图像进行组合，以形成具有边界细化模块的对称编码器/解码器FCNN的输入，以获取最终的头发分割输出。定量和定性结果均表明，我们的方法在LFW-Part和Figaro1k数据集上达到了最先进的性能。