Summary of Biometric Authentication Based on Enhanced Remote Photoplethysmography Signal Morphology, by Zhaodong Sun et al.
Biometric Authentication Based on Enhanced Remote Photoplethysmography Signal Morphology
by Zhaodong Sun, Xiaobai Li, Jukka Komulainen, Guoying Zhao
First submitted to arxiv on: 4 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Image and Video Processing (eess.IV); Signal Processing (eess.SP)
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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 paper introduces a novel approach to biometric authentication using remote photoplethysmography (rPPG) signals extracted from facial videos. The authors demonstrate that each individual’s rPPG signal morphology can be used as a unique identifier, similar to contact-based photoplethysmography (cPPG). To ensure facial privacy and utilize only the rPPG information for authentication, the team de-identifies facial videos to remove facial appearance while preserving the rPPG signals. The resulting rPPG signal morphology is then used to train a model for biometric authentication. The authors’ approach leverages unsupervised rPPG training followed by an rPPG-cPPG hybrid training stage, incorporating external cPPG datasets to enhance the accuracy of rPPG signal morphology recognition. Experimental results show that rPPG signals hidden in facial videos can be effectively used for biometric authentication. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a way to recognize people just by looking at their faces on video! This paper shows how to do this using special heart rate signals from facial videos, called remote photoplethysmography (rPPG). Each person has a unique heartbeat pattern that can be used like a fingerprint. To keep your face private, the team removes your face from the video while keeping the heartbeat signal. They then train a computer model to recognize these heartbeat patterns and use them for identification. The results show that this method is accurate and could be useful in many situations. |
Keywords
» Artificial intelligence » Unsupervised
