Biometric Authentication Eye Pose Optimization
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Solution Overview
Problem
Biometric authentication systems face challenges in capturing high-quality images of the eye region due to unwanted reflections, stray light, and varying user positions, which affect the accuracy and efficiency of biometric authentication and gaze tracking processes.
Innovation Solution
The implementation of flexible illumination methods and adaptive imaging systems that dynamically adjust lighting configurations and camera positions based on real-time feedback to optimize image capture, using multiple cameras and optical elements to improve image quality and robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the imaging system uses a fixed camera position and lighting configuration, then the device complexity is reduced, but the image quality and authentication accuracy deteriorate due to unwanted reflections and varying user positions
Solution Approach 1:
The patent implements dynamic adjustment of camera position and lighting configuration based on real-time eye pose detection. The system continuously monitors eye position and orientation, then automatically adjusts camera angles and lighting directions to optimize image quality while minimizing reflections and stray light. This dynamic adaptation resolves the contradiction by making the system flexible rather than fixed.
Solution Approach 2:
The system employs feedback loops where captured eye images are analyzed to determine current eye pose, which then informs subsequent camera and lighting adjustments. The feedback mechanism allows the system to learn from previous captures and continuously optimize imaging conditions, improving image quality without requiring overly complex manual configuration.
2Ease of operation
If the system prompts the user to manually adjust their pose or the device, then the manufacturing precision of the system is reduced, but the ease of operation improves
Solution Approach 1:
The system provides self-service by automatically detecting eye pose and making necessary adjustments without requiring user intervention. The automated pose detection and adjustment mechanisms eliminate the need for users to manually reposition themselves or the device, while maintaining high authentication accuracy through consistent optimal imaging conditions.
3Measurement precision
If the imaging system mechanically adjusts camera position or zoom, then the measurement precision of eye pose improves, but the productivity of the authentication process decreases due to additional adjustment steps
Solution Approach 1:
The system maintains continuous operation by performing pose detection and camera adjustment in an integrated, seamless workflow. Rather than pausing for manual intervention, the automated system continuously captures images, analyzes eye pose, and adjusts camera parameters without interrupting the authentication flow. This continuity preserves both measurement precision and productivity.
Solution Approach 2:
The system performs preliminary pose detection and camera positioning adjustments before the actual biometric authentication occurs. By pre-optimizing the imaging setup based on initial eye pose detection, the system ensures that subsequent authentication captures are taken under optimal conditions, eliminating the need for re-adjustments during the critical authentication phase.
Data Source
AI summary
Methods and apparatus for biometric authentication in which a current eye pose is determined and evaluated to determine if the current pose is satisfactory, and in which the eye pose may be improved by the user manually adjusting the device or their pose/gaze direction in response to a signal from the controller, and/or in which the imaging system is mechanically adjusted at the direction of the controller to improve the current view of the eye.