2D Gaze Estimation via 3D Projection and Implicit Calibration
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Solution Overview
Problem
Current gaze estimation systems face challenges such as high costs, poor inter-device compatibility, and the need for explicit calibration procedures, which negatively impact user experience and accuracy, especially in 2D gaze estimation models that struggle with generalization across different devices and user positions.
Innovation Solution
A system that includes a 3D gaze estimation module, a 3D to 2D projection module, and a user-specific parameter optimization module to estimate and convert 3D gaze directions into accurate 2D gaze estimation results using user-specific parameters optimized through calibration samples, allowing for automatic calibration and improved accuracy without assuming specific head positions or poses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If explicit calibration procedures are performed to improve gaze estimation accuracy, then measurement precision is improved, but ease of operation deteriorates due to the need for users to gaze at multiple calibration points
Solution Approach 1:
The system performs automatic calibration in the background before actual gaze estimation, preparing user-specific parameters and device-specific parameters in advance. This preliminary action eliminates the need for explicit calibration procedures during actual use, maintaining both high accuracy and good user experience.
Solution Approach 2:
The system uses implicit calibration where the device automatically collects calibration data from user interactions (clicks, touches) without requiring explicit user actions. The calibration process serves itself by utilizing naturally occurring user behavior data, eliminating the need for users to deliberately gaze at calibration points.
2Measurement precision
If 2D gaze estimation models are trained on specific datasets to improve accuracy on similar domains, then measurement precision is improved, but adaptability deteriorates when extending to different devices or user positions
Solution Approach 1:
The system transforms the problem from learning fixed 2D gaze parameters to learning 3D gaze parameters that can be adapted through parameter changes. By optimizing user-specific parameters and device-specific parameters separately, the system can adapt to different devices and user positions without retraining the entire model, significantly improving generalization ability.
Solution Approach 2:
The system transitions from direct 2D gaze estimation to 3D gaze estimation, adding a dimensional aspect that enables better generalization. By estimating gaze in 3D space and then projecting to 2D screen coordinates using optimized parameters, the system achieves both accuracy on training domains and adaptability to new domains.
3Measurement precision
If multiple head-mounted cameras or specialized gaze tracking systems are used to improve gaze estimation accuracy, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The system makes standard RGB cameras universal for gaze estimation by developing algorithms that work with conventional camera hardware. The same system can be deployed across different devices (smartphones, tablets, computers) using their built-in cameras, eliminating the need for specialized head-mounted cameras or dedicated gaze tracking hardware.
Solution Approach 2:
The system replaces complex mechanical gaze tracking systems with computational methods using standard camera hardware. By substituting specialized optical-mechanical systems with algorithmic processing of regular camera images, the system achieves comparable or superior accuracy with much simpler device requirements.
4Adaptability or versatility
If 3D gaze estimation is performed to improve generalization ability, then adaptability is improved, but ease of operation deteriorates due to the need for explicit calibration procedures
Solution Approach 1:
The system implements automatic calibration that performs self-service by collecting calibration data from natural user interactions without requiring explicit user actions. The calibration process happens implicitly in the background, maintaining ease of operation while enabling 3D gaze estimation for better generalization.
Data Source
AI summary
A system and a method for performing 2D on-screen user gaze estimation using an input facial image of a user captured using a camera associated to a processing device having a display. The method and system allow automated user calibration through automatic recording of calibration samples each including a calibration facial image of the user and an interaction point corresponding to a point on the display where an occurrence of a user interaction was detected when the corresponding calibration image was captured. The system and method also optimize user-specific parameters using the calibration samples by iteratively minimizing a total difference between the interaction points of a plurality of the calibration samples and corresponding 2D gaze estimation results and convert an estimated 3D gaze direction into a 2D gaze estimation result corresponding to a point on the display, by applying the users-specific parameters.