Eye Movement Tracking for Dynamic Security and Heat Map Analysis
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Solution Overview
Problem
Conventional systems fail to provide continuous user authentication and data protection on mobile devices, allowing unauthorized access and insufficiently evaluating user interactions with applications, which compromises privacy and user experience.
Innovation Solution
Implementing eye movement tracking to authenticate users, detect look-aways, and analyze interaction data to dynamically adjust access and obscure sensitive information, while using machine learning to generate heat maps for improving application design and user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional authentication systems are used to permit application access, then user access convenience is improved, but continuous data protection is lost allowing unauthorized viewing
Solution Approach 1:
The system performs preliminary authentication by capturing and storing eye image data before application access is granted. This pre-established biometric reference enables continuous verification without interrupting user workflow, resolving the contradiction between access convenience and protection continuity.
Solution Approach 2:
The system implements continuous eye movement tracking and authentication during application usage rather than only at initial access. This ongoing verification maintains data protection throughout the entire application session while remaining unobtrusive to user interaction.
2Reliability
If eye movement tracking is implemented for continuous authentication, then data security is improved, but device complexity increases
Solution Approach 1:
The system utilizes the device's existing camera and processor to perform eye tracking and authentication functions. By repurposing built-in hardware components rather than adding dedicated sensors or external devices, the solution maintains data protection continuity without significantly increasing device complexity.
3Reliability
If eye movement data is captured and analyzed to detect look-aways, then privacy protection is improved, but processing time and energy consumption increase
Solution Approach 1:
The system analyzes only critical eye movement parameters such as gaze direction and blink patterns rather than processing complete eye movement trajectories. This selective measurement approach provides sufficient privacy protection while reducing computational energy consumption.
Solution Approach 2:
The system performs eye movement analysis at periodic intervals rather than continuously monitoring every eye position. This periodic sampling detects look-aways effectively while minimizing processing energy consumption during application usage.
Data Source
AI summary
Systems for enhanced protection or security using eye movement tracking are provided. In some examples, a user may launch an application on a user device. If enhanced protections apply to the application, an image of a user eye may be captured and compared to pre-stored data to ensure the user is a registered user. Additional eye movement data may be captured and analyzed. Analyzing the eye movement data may include detecting a look-away by a user. If a look-away is detected, the application may be closed, data within the application may be obscured, or the like. Eye movement data may also be analyzed to identify portions of an application the user viewed for more than a threshold time period, less than a threshold time period, and the like. This data may be analyzed (e.g., using machine learning) to generate one or more heat maps.