Summary of One-frame Calibration with Siamese Network in Facial Action Unit Recognition, by Shuangquan Feng and Virginia R. De Sa
One-Frame Calibration with Siamese Network in Facial Action Unit Recognition
by Shuangquan Feng, Virginia R. de Sa
First submitted to arxiv on: 30 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 This research proposes a novel approach to facial action unit (AU) recognition by introducing one-frame calibration (OFC). The existing AU recognition systems aim for cross-participant non-calibrated generalization, but this new strategy uses a single image of an unseen face’s neutral expression as the reference image for calibration. The proposed Calibrating Siamese Network (CSN) with a simple iResNet-50 backbone is demonstrated to be highly effective on the DISFA, DISFA+, and UNBC-McMaster datasets. The results show that the OFC CSN-IR50 model outperforms state-of-the-art models for both AU intensity estimation and AU detection, while mitigating facial attribute biases such as wrinkles, eyebrow positions, facial hair, etc. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research develops a new way to recognize facial expressions by using just one picture of someone’s neutral face. Right now, most facial expression recognition systems try to work with faces they’ve never seen before without adjusting anything. But this approach recognizes that every person’s face is different and that can make it hard to accurately tell what emotions someone is showing. So, the researchers propose taking a single image of someone’s neutral face as a reference point to help get better results. They also develop a special kind of network called the Calibrating Siamese Network (CSN) with a simple iResNet-50 backbone that does really well on some standard datasets. This new approach can help reduce mistakes and give more accurate results for recognizing facial expressions. |
Keywords
* Artificial intelligence * Generalization * Siamese network
