Facial Video-based rPPG Estimation
CONTEXT
Physiological signals, e.g., heart rate and respiratory rate are important vital signals that can find their utilities in a wide range of applications, including healthcare systems, rescue activities, and human emotion analysis. While traditional methods require physical contact along with specialized equipment to properly work, rPPG (remote photoplethysmography) with great potential as the most preferable one needs a mere camera to effectively and efficiently capture such signals. Based just on these simple settings, more and more related extensions have been discovered beyond the original apps, namely Face Anti-spoofing, Human micro-Expression Analysis, and Human Public-Relationship.
Task
In this work, students are expected to review state-of-the-art work around this research briefly and then carefully analyze the strengths and weaknesses of respective ones. From that, they are positively encouraged to propose their own novel solutions that can significantly improve the fidelity and efficiency of the prior work.
YOU WILL GET
- Profound experience in related fields, such as deep learning models, time-series data properties, and human vital signal indication.
- A publication at top-tier AI venues if the work is qualified.
REQUIREMENTS
- Strong background in Machine Learning and Deep Learning.
- At least familiar with one of the AI frameworks, such as Tensorflow, and Pytorch.
- Having hands-on experience in basic Deep Learning projects.
- A good comprehension of related papers.
Contact:
Minh Son Nguyen, m.s.nguyen@utwente.nl