Eye-Gaze Door Access Using Long-Range Intent Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current access control systems inaccurately determine user intent due to reliance on proximity, leading to unnecessary door openings, increased security risks, wear, and power consumption, and existing eye tracking systems require user-specific calibration and are limited by distance and head movement.
Innovation Solution
A gaze-based system using a camera and infrared illuminator for long-range gaze estimation, capturing eye movements at distances up to 10 meters without calibration, integrating gaze status with head orientation to categorize intent into simplified classes, and employing machine learning models like CNNs to predict door opening intentions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If proximity-based access control is used, then door opening is automated, but user intent is inaccurately determined leading to unnecessary door openings
Solution Approach 1:
The system segments the intent detection process into multiple independent analysis components: proximity detection, gaze direction analysis, head orientation detection, and temporal pattern recognition. Each component processes specific visual features separately before integrating results to determine overall intent, improving accuracy by avoiding conflation of different detection signals.
Solution Approach 2:
The system introduces an intermediary machine learning model that processes raw visual data from cameras and transforms it into interpreted intent signals. This intermediary layer analyzes patterns in gaze status, head orientation, and temporal sequences to accurately determine user intent before triggering door opening, preventing false activations from simple proximity detection.
2Measurement precision
If traditional eye tracking is used, then gaze direction can be detected, but user-specific calibration and head movement limitations are required
Solution Approach 1:
The system employs a universal machine learning model trained on diverse eye movement patterns from multiple users. This single model handles gaze detection for all users without requiring individual calibration, making the system universally applicable while maintaining accurate gaze direction detection across different users and head positions.
Solution Approach 2:
The system changes the detection parameters from fixed geometric relationships (which require calibration) to pattern recognition based on temporal sequences of gaze status and head orientation. By analyzing how gaze and head movement evolve over time rather than relying on static calibrated parameters, the system eliminates calibration requirements while maintaining detection precision.
3Measurement precision
If short-distance eye tracking is used, then gaze can be detected, but effective range is limited to less than 10 meters
Solution Approach 1:
The system transitions from two-dimensional retinal image analysis to three-dimensional spatial reasoning by incorporating head orientation detection alongside gaze status. This dimensional expansion allows the system to accurately interpret gaze direction from longer distances by combining eye position with head pose information, extending effective detection range beyond traditional close-range eye tracking.
Solution Approach 2:
The system replaces the mechanical/optical focus requirements of traditional eye tracking with computational image processing and machine learning analysis. This substitution allows gaze detection to function effectively at longer distances where optical focus becomes more challenging, maintaining detection accuracy across extended ranges up to 10 meters and beyond.
4Ease of operation
If frequent door openings occur, then access is convenient, but wear and power consumption increase
Solution Approach 1:
The system implements feedback through temporal analysis of gaze and head orientation patterns. It continuously monitors user behavior and only triggers door opening when sustained intent patterns are detected, rather than responding to every momentary gaze. This feedback mechanism maintains access convenience by responding to genuine intent while reducing unnecessary activations that waste energy and cause wear.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Accurately determines user intent for secure area access, reducing unnecessary openings, enhancing security, and optimizing power usage by eliminating the need for user-specific calibration and extending effective range.
Implementation Method 1
A gaze-based system using a camera and infrared illuminator for long-range gaze estimation
Data Source
AI summary
Systems and techniques may be used for allowing a person access to a secure area, opening a door, or the like. A technique may include detecting a person within a particular distance of a secure area, capturing, using a camera, a set of images of at least a facial feature of the person, preprocessing the set of images to identify the facial feature in at least one image of the set of images, and identifying, using processing circuitry, a gaze status of the person based on the identified facial feature in the at least one image. The technique may include determining, using a trained machine learning model, an intent of the person to access the secure area based on the gaze status, and based on determining the intent, granting access to the secure area