# End-to-End Face Parsing via Interlinked Convolutional Neural Networks

arXiv: https://arxiv.org/abs/2002.04831 · HTML: https://mlsanity.com/paper/2002.04831/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Feb 2020.

## Built on by (most cited first, 6 of 6)

- [Fooling thermal infrared pedestrian detectors in real world using small bulbs](https://mlsanity.com/paper/2101.08154/) (2021)
- [AGRNet: Adaptive Graph Representation Learning and Reasoning for Face Parsing](https://mlsanity.com/paper/2101.07034/) (2021)
- [Trilateral Attention Network for Real-time Medical Image Segmentation](https://mlsanity.com/paper/2106.09201/) (2021)
- [MIXPGD: Hybrid Adversarial Training for Speech Recognition Systems](https://mlsanity.com/paper/2303.05758/) (2023)
- [Occlusion-Aware Deep Convolutional Neural Network via Homogeneous Tanh-transforms for Face Parsing](https://mlsanity.com/paper/2308.15323/) (2023)
- [Learning Contour-Guided 3D Face Reconstruction with Occlusions](https://mlsanity.com/paper/2503.12494/) (2025)

## Abstract (arXiv)

Face parsing is an important computer vision task that requires accurate pixel segmentation of facial parts (such as eyes, nose, mouth, etc.), providing a basis for further face analysis, modification, and other applications. Interlinked Convolutional Neural Networks (iCNN) was proved to be an effective two-stage model for face parsing. However, the original iCNN was trained separately in two stages, limiting its performance. To solve this problem, we introduce a simple, end-to-end face parsing framework: STN-aided iCNN(STN-iCNN), which extends the iCNN by adding a Spatial Transformer Network (STN) between the two isolated stages. The STN-iCNN uses the STN to provide a trainable connection to the original two-stage iCNN pipeline, making end-to-end joint training possible. Moreover, as a by-product, STN also provides more precise cropped parts than the original cropper. Due to these two advantages, our approach significantly improves the accuracy of the original model. Our model achieved competitive performance on the Helen Dataset, the standard face parsing dataset. It also achieved superior performance on CelebAMask-HQ dataset, proving its good generalization. Our code has been released at https://github.com/aod321/STN-iCNN.