# Occlusion-Aware Deep Convolutional Neural Network via Homogeneous Tanh-transforms for Face Parsing

arXiv: https://arxiv.org/abs/2308.15323 · HTML: https://mlsanity.com/paper/2308.15323/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 44 references that are in the map.
- First posted on arXiv 29 Aug 2023.

## Builds on (most cited first, 19 of 19)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [MaskGAN: Towards Diverse and Interactive Facial Image Manipulation](https://mlsanity.com/paper/1907.11922/) (2019)
- [Learning Deconvolution Network for Semantic Segmentation](https://mlsanity.com/paper/1505.04366/) (2015)
- [Simultaneous Detection and Segmentation](https://mlsanity.com/paper/1407.1808/) (2014)
- [Deep Occlusion-Aware Instance Segmentation with Overlapping BiLayers](https://mlsanity.com/paper/2103.12340/) (2021)
- [Joint Optic Disc and Cup Segmentation Based on Multi-label Deep Network and Polar Transformation](https://mlsanity.com/paper/1801.00926/) (2018)
- [Equivariant Transformer Networks](https://mlsanity.com/paper/1901.11399/) (2019)
- [Face Parsing with RoI Tanh-Warping](https://mlsanity.com/paper/1906.01342/) (2019)
- [RoI Tanh-polar Transformer Network for Face Parsing in the Wild](https://mlsanity.com/paper/2102.02717/) (2021)
- [Interlinked Convolutional Neural Networks for Face Parsing](https://mlsanity.com/paper/1806.02479/) (2018)
- [Mask-FPAN: Semi-Supervised Face Parsing in the Wild With De-Occlusion and UV GAN](https://mlsanity.com/paper/2212.09098/) (2022)
- [Face Parsing via Recurrent Propagation](https://mlsanity.com/paper/1708.01936/) (2017)
- [Towards Learning Structure via Consensus for Face Segmentation and Parsing](https://mlsanity.com/paper/1911.00957/) (2019)
- [Face Parsing via a Fully-Convolutional Continuous CRF Neural Network](https://mlsanity.com/paper/1708.03736/) (2017)
- [End-to-End Face Parsing via Interlinked Convolutional Neural Networks](https://mlsanity.com/paper/2002.04831/) (2020)

## Abstract (arXiv)

Face parsing infers a pixel-wise label map for each semantic facial component. Previous methods generally work well for uncovered faces, however, they overlook facial occlusion and ignore some contextual areas outside a single face, especially when facial occlusion has become a common situation during the COVID-19 epidemic. Inspired by the lighting phenomena in everyday life, where illumination from four distinct lamps provides a more uniform distribution than a single central light source, we propose a novel homogeneous tanh-transform for image preprocessing, which is made up of four tanh-transforms. These transforms fuse the central vision and the peripheral vision together. Our proposed method addresses the dilemma of face parsing under occlusion and compresses more information from the surrounding context. Based on homogeneous tanh-transforms, we propose an occlusion-aware convolutional neural network for occluded face parsing. It combines information in both Tanh-polar space and Tanh-Cartesian space, capable of enhancing receptive fields. Furthermore, we introduce an occlusion-aware loss to focus on the boundaries of occluded regions. The network is simple, flexible, and can be trained end-to-end. To facilitate future research of occluded face parsing, we also contribute a new cleaned face parsing dataset. This dataset is manually purified from several academic or industrial datasets, including CelebAMask-HQ, Short-video Face Parsing, and the Helen dataset, and will be made public. Experiments demonstrate that our method surpasses state-of-the-art methods in face parsing under occlusion.