Dynamic Eye-Opening Threshold for Human-Eye State Detection
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Solution Overview
Problem
Traditional human-eye state detection methods struggle to accurately determine eye state due to variations in eye size among individuals, environmental factors, and external interference such as glasses or changes in light.
Innovation Solution
A human-eye state detection device and method that continuously updates the eye-opening threshold values for both eyes, dynamically adjusting these thresholds to reduce environmental impact and improve accuracy, regardless of eye size or external factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional fixed threshold methods are used for eye state detection, then the detection process is simple, but the accuracy is poor due to variations in eye size and environmental factors
Solution Approach 1:
The patent applies dynamics by transforming the fixed threshold into a dynamic, adaptive threshold that automatically adjusts to each user's eye characteristics. The system captures multiple facial images, calculates eye-opening degrees, and iteratively updates the threshold until convergence, allowing the detection system to adapt to individual variations in eye size, shape, and environmental conditions while maintaining operational simplicity through automated adaptation.
2Reliability
If dynamic threshold adjustment is implemented to reduce environmental impact, then the detection accuracy improves, but the computational complexity increases
Solution Approach 1:
The patent implements feedback by continuously monitoring the eye-opening degree and comparing it against the adaptive threshold, then using this information to iteratively refine the threshold value. The system captures facial images, calculates the eye-opening degree, compares it with the current threshold, and updates the threshold based on the comparison results until convergence is achieved, creating a closed-loop feedback system that improves reliability under varying environmental conditions.
Solution Approach 2:
The patent applies preliminary action by performing threshold initialization and adaptive learning during a calibration phase before actual eye state detection begins. The system pre-adjusts the threshold to match the user's specific eye characteristics through iterative calculation and convergence, so that when actual detection is needed, the system already has an optimized threshold ready, reducing computational complexity during real-time operation.
3Measurement precision
If individualized threshold calibration is performed for each user, then the detection accuracy for different eye sizes improves, but the time required for setup increases
Solution Approach 1:
The patent applies preliminary action by performing the individualized threshold calibration during an initial setup or idle period before actual eye state monitoring begins. The system captures multiple facial images during this preliminary phase, iteratively calculates and converges the adaptive threshold, and stores it for use during actual operation. This separates the time-consuming calibration process from the real-time detection process, minimizing the perceived setup time for users.
Solution Approach 2:
The patent implements self-service by designing the calibration process to automatically capture facial images, calculate eye-opening degrees, and iteratively adjust thresholds without requiring manual user input or intervention. The system performs self-calibration by autonomously capturing images during normal operation, computing the adaptive threshold through iterative convergence, and applying it immediately, thereby eliminating the need for separate calibration sessions and reducing perceived setup time.
Data Source
AI summary
A human-eye state detection device is provided, which includes an image capturing device and a processor. The image capturing device is configured to continuously capture a plurality of frames of facial images of a user. The processor determines an eye area from the frames of facial images, and calculates an eye-opening average based on the eye area. The processor repeatedly updates the eye-opening threshold based on the average eye-opening value. In response to the average eye-opening value being less than the eye-opening threshold, the processor determines that the user is in an eye-closing state.