Research publication
Research that helps face-recognition systems reject fake inputs
Published deep-learning research on distinguishing live faces from presentation attacks such as photos, videos, and masks.
- Fact 1
- 80,000 frames
- Fact 2
- Multi-modal data
- Fact 3
- Published by IEEE
- Period
- 2022
The problem
Face-recognition systems can be fooled by presentation attacks. Security-critical deployments need a reliable way to distinguish a live person from a fake input.
What I built
Working with a research team, I developed and evaluated deep-learning methods on a multi-modal dataset of 80,000 frames, including data focused on South Asian ethnicity.
The result
The work achieved high detection accuracy and was published through IEEE, contributing practical evidence to biometric-security research.
Technical details
For technical reviewers, the delivery used the following tools and platforms. The business problem and result remain the primary scope.
- Deep learning
- Computer vision
- Transformer models
- RGB and thermal data