Eyetracker Pupil Detection Using Light-Intensity Ranges
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Solution Overview
Problem
Existing eyetracking technologies require complex algorithms that consume significant processing resources and energy, making them inefficient for consumer products.
Innovation Solution
A method in an eyetracker that determines a range of pupil sizes based on light-intensity information, using an eye model and calibration procedures to limit potential pupil candidates, thereby reducing processing resources needed for accurate pupil detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex algorithms are used for pupil detection, then measurement precision is improved, but use of energy increases
Solution Approach 1:
The patent applies preliminary action by performing calibration procedures before actual eyetracking to establish pupil size characteristics. The system pre-determines expected pupil size ranges under various lighting conditions, so that during operation, only pupils within these pre-established ranges need to be considered. This eliminates the need for complex real-time analysis of all potential eye regions, significantly reducing processing energy while maintaining detection accuracy.
Solution Approach 2:
The patent changes parameters by using light-intensity information to dynamically adjust the search parameters for pupil detection. Instead of using fixed complex algorithms, the system varies the pupil size search range based on measured lighting conditions and calibration data. This parameter adaptation allows simpler detection algorithms to achieve the same precision as complex fixed algorithms, reducing energy consumption.
2Measurement precision
If complex algorithms are used for pupil detection, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The calibration procedure performed in advance captures pupil size characteristics under different lighting conditions, storing this information for later use. This preliminary characterization transforms a potentially complex real-time detection problem into a simpler search problem constrained by pre-established parameters, reducing algorithmic complexity while preserving detection precision.
Solution Approach 2:
The patent introduces light-intensity information as an intermediary parameter that mediates between the imaging system and pupil detection algorithm. By using lighting conditions as an intermediate variable to constrain the search space, the system avoids directly implementing complex algorithms, instead using the intermediary light-intensity data to guide simpler detection processes.
3Measurement precision
If the search space for pupil candidates is not constrained, then measurement precision is improved, but productivity decreases
Solution Approach 1:
The system changes the search parameter ranges dynamically based on light-intensity information and calibration data. By adjusting the pupil size search bounds according to expected physiological responses to lighting conditions, the algorithm concentrates computational effort on the most likely pupil locations and sizes, achieving both high precision and efficient processing.
Solution Approach 2:
Calibration procedures are performed in advance to establish expected pupil size ranges, creating a lookup table or parameter set that guides the actual detection process. This preliminary preparation enables the system to quickly identify valid pupil candidates during operation without exhaustive search, improving processing efficiency while maintaining detection accuracy.
Data Source
AI summary
An eyetracker obtains a digital image representing at least one eye of a subject. The eyetracker then searches for pupil candidates in the digital image according to a search algorithm and, based on the searching, determines a position for the at least one eye in the digital image. The eyetracker also obtains light-intensity information expressing an estimated amount of light energy exposing the at least one eye when registering the digital image. In response to the light-intensity information, the eye-tracker determines a range of pupil sizes. The search algorithm applies the range of pupil sizes in such a manner that a detected pupil candidate must have size within the range of pupil sizes to be accepted by the search algorithm as a valid pupil of the subject.