Summary of Spatial Action Unit Cues For Interpretable Deep Facial Expression Recognition, by Soufiane Belharbi et al.
Spatial Action Unit Cues for Interpretable Deep Facial Expression Recognition
by Soufiane Belharbi, Marco Pedersoli, Alessandro Lameiras Koerich, Simon Bacon, Eric Granger
First submitted to arxiv on: 1 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a new learning strategy to train deep interpretable models for facial expression recognition (FER). The strategy explicitly incorporates spatial action units (AUs) into classifier training, allowing for the visual interpretation of expressions. A AU codebook is used along with input image expression labels and facial landmarks to construct a AU heatmap indicating discriminative image regions. This spatial cue is leveraged to train a deep interpretable classifier for FER by constraining spatial layer features to be correlated with AU heatmaps. The strategy relies only on image class expression for supervision, without additional manual annotations. Evaluation on two public benchmarks RAF-DB and AffectNet datasets shows that the proposed strategy improves layer-wise interpretability without degrading classification performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making machines better at understanding facial expressions. Right now, computers are really good at recognizing emotions, but they don’t show us how they got to that answer. The researchers propose a new way of training machines so they can not only recognize emotions but also explain why they made that decision. This is useful because humans often want to know what makes a machine’s predictions accurate or inaccurate. The new approach uses special maps to highlight the most important parts of an image when it comes to recognizing facial expressions. |
Keywords
* Artificial intelligence * Classification
