# Point Adversarial Self Mining: A Simple Method for Facial Expression Recognition

arXiv: https://arxiv.org/abs/2008.11401 · HTML: https://mlsanity.com/paper/2008.11401/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Aug 2020.

## Built on by (most cited first, 11 of 11)

- [AGKD-BML: Defense Against Adversarial Attack by Attention Guided Knowledge Distillation and Bi-directional Metric Learning](https://mlsanity.com/paper/2108.06017/) (2021)
- [PAtt-Lite: Lightweight Patch and Attention MobileNet for Challenging Facial Expression Recognition](https://mlsanity.com/paper/2306.09626/) (2023)
- [Semantic-Preserving Adversarial Text Attacks](https://mlsanity.com/paper/2108.10015/) (2021)
- [Pro-UIGAN: Progressive Face Hallucination from Occluded Thumbnails](https://mlsanity.com/paper/2108.00602/) (2021)
- [Exploring Robust Features for Improving Adversarial Robustness](https://mlsanity.com/paper/2309.04650/) (2023)
- [Dynamic Adaptive Threshold based Learning for Noisy Annotations Robust Facial Expression Recognition](https://mlsanity.com/paper/2208.10221/) (2022)
- [A Perception CNN for Facial Expression Recognition](https://mlsanity.com/paper/2512.06422/) (2025)
- [VidFace: A Full-Transformer Solver for Video FaceHallucination with Unaligned Tiny Snapshots](https://mlsanity.com/paper/2105.14954/) (2021)
- [Quantified Facial Expressiveness for Affective Behavior Analytics](https://mlsanity.com/paper/2110.01758/) (2021)
- [Unsupervised Domain Adaptive Person Re-Identification via Human Learning Imitation](https://mlsanity.com/paper/2111.14014/) (2021)
- [RARITYNet: Rarity Guided Affective Emotion Learning Framework](https://mlsanity.com/paper/2205.08595/) (2022)

## Abstract (arXiv)

In this paper, we propose a simple yet effective approach, named Point Adversarial Self Mining (PASM), to improve the recognition accuracy in facial expression recognition. Unlike previous works focusing on designing specific architectures or loss functions to solve this problem, PASM boosts the network capability by simulating human learning processes: providing updated learning materials and guidance from more capable teachers. Specifically, to generate new learning materials, PASM leverages a point adversarial attack method and a trained teacher network to locate the most informative position related to the target task, generating harder learning samples to refine the network. The searched position is highly adaptive since it considers both the statistical information of each sample and the teacher network capability. Other than being provided new learning materials, the student network also receives guidance from the teacher network. After the student network finishes training, the student network changes its role and acts as a teacher, generating new learning materials and providing stronger guidance to train a better student network. The adaptive learning materials generation and teacher/student update can be conducted more than one time, improving the network capability iteratively. Extensive experimental results validate the efficacy of our method over the existing state of the arts for facial expression recognition.