Eye Tracking Device Automatic User Identification
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Solution Overview
Problem
Existing eye tracking devices require users to manually register profiles, perform calibration, and select profiles each time they use the device, which is time-consuming and prone to errors, especially in multi-user scenarios, hindering the integration of eye tracking technology into various applications.
Innovation Solution
An automatic user identification method using an eye tracking device that captures user identification data, compares it with stored profiles, and automatically selects the correct calibration data, eliminating the need for manual profile selection and calibration, and allowing implicit calibration during usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual profile registration and selection is required, then user identification accuracy is improved, but user effort and time consumption increase significantly
Solution Approach 1:
The system performs automatic user identification by capturing facial features and comparing them with stored profiles without requiring manual user input. The eye tracking device autonomously determines which profile to load based on facial recognition, eliminating the need for users to manually register or select profiles.
Solution Approach 2:
User profiles with facial features are pre-stored in the system before actual use. When a user approaches the device, the system already has the facial data ready for comparison, enabling rapid automatic identification without requiring real-time manual registration or selection.
2Measurement precision
If manual profile selection is required, then system control precision is improved, but ease of operation deteriorates significantly
Solution Approach 1:
The system automatically determines profile selection based on facial recognition without requiring user intervention. The processing unit compares captured facial features with stored profiles and autonomously loads the correct calibration data, making the system as easy to use as simply approaching the device.
3Measurement precision
If classical calibration procedure is used, then measurement precision is improved, but device complexity and operation complexity increase
Solution Approach 1:
The patent combines automatic user identification with calibration data loading into a single integrated process. When the system automatically identifies a user through facial recognition, it simultaneously loads the corresponding pre-stored calibration data, merging what were previously separate manual steps into one automated operation.
Data Source
AI summary
The invention relates to a method for automatically identifying at least one user of an eye tracking device (10) by means of the eye tracking device (10), wherein user identification data of the at least one user are captured by a capturing device (14) of the eye tracking device (10). Under the first condition that at least one profile (P1, P2, P3) with associated identification data (I1, I2, I3) of at least one specific user is stored in a storage medium (20), the stored identification data (I1, I2, I3) of the at least one profile (P1, P2, P3) are compared with the captured user identification data, and under the second condition that the captured user identification data match the stored identification data (I1, I2, I3) according to a predefined criterion, the at least one user is identified as the at least one specific user, for whom the at least one profile (P1, P2, P3) was stored.