Facial Recognition Liveness Detection for Fraudulent Check-Ins
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Solution Overview
Problem
Existing monitoring systems are vulnerable to fraudulent check-ins by monitored individuals using static images or prerecorded videos, which undermines the validity of verification processes.
Innovation Solution
A hybrid monitoring system comprising a user attached and user detached monitor device, equipped with frame rate control modules and image processing algorithms, to detect live individuals by analyzing eyelid movement and artefacts in captured images, and a central monitoring station for data analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional video calling is used for check-in verification, then the process is simple and easy to operate, but it becomes vulnerable to fraudulent check-ins using static images or prerecorded videos
Solution Approach 1:
The system performs preliminary actions by capturing multiple images at different time points before the actual verification decision is made. These pre-captured images are then analyzed to detect signs of fraud such as static images or prerecorded videos, preventing fraudulent check-ins before they can be processed as valid verifications
Solution Approach 2:
An image analysis module is introduced as an intermediary component between the video calling system and the verification process. This intermediary analyzes the captured images for authenticity markers and provides a fraud detection assessment, adding a layer of security without fundamentally redesigning the entire verification system
2Measurement precision
If multiple images are captured and analyzed to detect fraud, then the detection accuracy improves, but the processing time and computational resources increase
Solution Approach 1:
The system captures more images than strictly necessary for basic verification (excessive action), but then applies selective analysis only to images that show potential fraud indicators. This partial analysis approach ensures high detection accuracy for fraudulent attempts while minimizing unnecessary processing time for legitimate check-ins
3Reliability
If frame rate control and image timing adjustment are implemented, then the ability to detect prerecorded videos improves, but the device complexity increases
Solution Approach 1:
The system dynamically changes the frame rate and timing parameters of image capture to create a verification pattern that is difficult to replicate in prerecorded videos. By adjusting these parameters in real-time during the verification process, the system creates temporal variations that indicate live imaging, adding reliability without requiring complex hardware modifications
Data Source
AI summary
Various embodiments provide systems and methods for validating imaging of a monitored individual.