Summary of Lafs: Landmark-based Facial Self-supervised Learning For Face Recognition, by Zhonglin Sun et al.
LAFS: Landmark-based Facial Self-supervised Learning for Face Recognition
by Zhonglin Sun, Chen Feng, Ioannis Patras, Georgios Tzimiropoulos
First submitted to arxiv on: 13 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 A novel self-supervised pretraining approach is proposed to learn facial representations that can be adapted for effective face recognition models without labeled data. The method, dubbed LAndmark-based Facial Self-supervised learning (LAFS), utilizes patches localized by extracted facial landmarks instead of random cropping blocks, which enables the learning of key representations critical for face recognition. Two landmark-specific augmentations are introduced to regularize the learning process. The proposed approach achieves significant improvement over state-of-the-art methods on multiple face recognition benchmarks, particularly in few-shot scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Face recognition models can be trained effectively using self-supervised pretraining and facial landmarks without labeled data. A new method called LAFS uses patches localized by extracted facial landmarks instead of random cropping blocks to learn key representations critical for face recognition. This approach achieves better results than current methods on various face recognition benchmarks, especially when there is limited training data. |
Keywords
» Artificial intelligence » Face recognition » Few shot » Pretraining » Self supervised
