Liveness Detection via Eye Tracking Correlation
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Solution Overview
Problem
Current facial recognition systems lack effective methods to differentiate between a live human subject and a spoofed image, such as a printed picture or video, which can lead to unauthorized access and security breaches.
Innovation Solution
A liveness detection system that uses eye and head tracking by introducing a graphical item moving randomly on a screen, requiring the user to follow it, and calculates a correlation score to determine if the user's eye and head movements match the path of the item, thereby verifying the user's liveliness based on a threshold score.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If facial recognition is used to verify a person from a digital image, then identification speed is improved, but the system becomes vulnerable to spoofing attacks using printed pictures or videos
Solution Approach 1:
The system performs preliminary liveness detection by analyzing eye movement characteristics before final authentication. A graphical indicator is displayed and the system captures eye movement data in advance, calculating correlation scores to determine if the subject is alive before completing the authentication process, thereby preventing spoofing attacks
Solution Approach 2:
The system uses feedback from eye tracking technology to continuously monitor and analyze the correlation between graphical indicator movement and actual eye movement. This feedback mechanism allows the system to dynamically assess liveness and adjust authentication decisions based on real-time biological response data
2Reliability
If eye tracking is implemented to detect liveness, then anti-spoofing capability is improved, but device complexity increases
Solution Approach 1:
The system uses the existing front-facing camera for multiple purposes: capturing facial images for recognition, tracking eye movements for liveness detection, and monitoring graphical indicator interactions. This multi-functional approach enables anti-spoofing capability without requiring separate dedicated hardware components
Solution Approach 2:
The system leverages the subject's own biological eye movements as the authentication mechanism. The natural reflexive eye tracking behavior serves as both the challenge and the verification method, eliminating the need for external sensors or complex additional devices
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for liveness detection are disclosed. In one aspect, a method includes the actions of providing, for display on a screen to a user, a graphical item that travels along a path. The actions further include tracking a movement of an eye of the user while the graphical item travels along the path on the screen. The actions further include comparing the movement of the eye of the user to the path traveled by the graphical item. The actions further include generating an eye correlation score that reflects a correlation between the movement of the eye of the user and the path traveled by the graphical item. The actions further include determining whether the eye correlation score satisfies an eye liveness threshold score. The actions further include determining whether the user is a live person.