Wrinkle Detection in Facial Images Using Multi-Angle Rotation
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Solution Overview
Problem
Existing wrinkle detection methods in facial images face challenges in accurately detecting wrinkles due to irregular directions, resulting in low detection precision.
Innovation Solution
A method that involves rotating the region of interest in a facial image to obtain multiple images at different angles, determining wrinkle points based on grayscale values, and identifying wrinkle lines to improve detection precision by considering various orientations and thicknesses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If wrinkle detection is performed using existing methods on facial images, then the detection process is simple, but detection precision is low due to irregular wrinkle directions
Solution Approach 1:
The detection process is segmented into multiple stages: first rotating the image to obtain multiple images at different angles, then detecting wrinkle points in each rotated image separately, and finally synthesizing the results. This segmentation allows the system to handle irregular wrinkle directions by processing them in oriented segments rather than attempting to detect all directions simultaneously in a single complex operation.
Solution Approach 2:
The patent introduces an angular dimension by rotating the facial image into multiple images at different angles (0°, 45°, 90°, 135°). This transforms the original two-dimensional detection problem into a multi-angular detection space, enabling the system to capture wrinkles in various directions that would be missed in a single static image analysis.
2Measurement precision
If the rectangular window size is increased to detect thicker wrinkles, then detection coverage improves, but detection precision for thin wrinkles deteriorates
Solution Approach 1:
The patent applies different detection parameters (rectangular window sizes) for different detection scenarios. By using multiple window sizes (e.g., 3×3, 5×5, 7×7 pixels) corresponding to different wrinkle thicknesses, the system can adaptively select appropriate detection sensitivity for local regions, ensuring both thin and thick wrinkles are detected with optimal precision.
Solution Approach 2:
The detection algorithm dynamically adjusts the rectangular window size parameter based on the detected wrinkle characteristics. When thin wrinkles are detected, smaller window sizes are used to maintain precision; when thicker wrinkles are present, larger window sizes are applied to ensure adequate coverage and detection accuracy.
Data Source
AI summary
A wrinkle detection method includes: rotating a region, in a face image, in which a wrinkle needs to be detected, to obtain a plurality of to-be-detected images; determining wrinkle points from all pixel points based on grayscale values of the pixel points in each of the to-be-detected images with different angles; determining at least one wrinkle line based on the wrinkle points; and then displaying, by the electronic device, the wrinkle line in the region in which the wrinkle needs to be detected, where each wrinkle line indicates one wrinkle in each of the to-be-detected images.