Face Tracking via Dynamic Color Parameter Binarization
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Solution Overview
Problem
Existing face tracking technologies struggle to maintain accuracy in the presence of environmental changes, as they rely on fixed physiological features like skin color, which can be affected by varying greyscale in images.
Innovation Solution
A face tracking method that performs binarization processing using dynamic standard ranges of color parameters, specifically hue, saturation, and brightness, which are updated based on actual pixel values in each image, allowing the system to adapt to environmental changes by averaging reference ranges for hue, saturation, and brightness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If fixed physiological features (skin color) are used for face tracking, then the tracking method is simple to implement, but the tracking accuracy decreases when environmental factors change
Solution Approach 1:
The patent applies dynamics by transitioning from fixed skin color thresholds to dynamic color parameter ranges. The system continuously updates the standard range of color parameters (hue, saturation, brightness) based on actual pixel values from the initial facial image, allowing the tracking algorithm to adapt to environmental changes while maintaining implementation simplicity through automated parameter adjustment.
Solution Approach 2:
The patent implements parameter changes by modifying the color parameter ranges used for binarization processing. Instead of using fixed thresholds, the system calculates actual color parameter values from the initial facial image and dynamically adjusts the standard range of hue, saturation, and brightness parameters, thereby maintaining tracking accuracy under varying environmental conditions.
2Measurement precision
If dynamic color parameter ranges are used for binarization processing, then the tracking accuracy is maintained under environmental changes, but the computational complexity increases
Solution Approach 1:
The patent applies self-service by enabling the system to automatically update its own color parameter standards without external intervention. The algorithm extracts color parameter information from the initial facial image itself and uses this information to adjust the binarization thresholds, making the system self-adapting to environmental conditions while reducing the need for complex external calibration mechanisms.
Solution Approach 2:
The patent implements preliminary action by pre-calculating the standard range of color parameters from the initial facial image before the actual tracking process begins. This preliminary parameter extraction and standardization reduces the computational burden during real-time tracking, as the system only needs to compare current frames against the pre-established dynamic thresholds rather than performing complex calculations on every frame.
3Adaptability or versatility
If the standard range of color parameter is updated based on actual pixel values, then the system adapts to environmental changes, but the processing time increases
Solution Approach 1:
The patent applies preliminary action by updating the color parameter standards during the initial face detection phase rather than during continuous tracking. By extracting and standardizing color parameters from the initial facial image once, the system establishes adaptive thresholds that can be reused throughout the tracking process, thereby achieving environmental adaptability without incurring continuous processing overhead.
Solution Approach 2:
The patent implements dynamics by making the color parameter standards adaptive rather than static. The system dynamically determines the standard range of hue, saturation, and brightness parameters based on the actual distribution of color values in the initial facial image, allowing the tracking algorithm to automatically adjust to environmental conditions while maintaining efficient processing through standardized comparison operations.
Data Source
AI summary
Disclosed is face tracking method and device. The method includes: acquiring an initial facial image in a to-be-tracked picture; performing binarization processing on the initial facial image according to a standard range of color parameter and an actual value of the color parameter of each pixel in the initial facial image, to obtain a binarized facial image; acquiring a position of a preset organ in the binarized facial image; and acquiring a position of a final facial image according to the position of the preset organ and a position of the initial facial image.