Facial Stroke Detection Using Feature Symmetry and Classifier
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Solution Overview
Problem
Traditional facial stroking detection methods and systems have low accuracy rates due to reliance on expressional asymmetry and mouth alignment, often missing the best treatment time for patients.
Innovation Solution
A facial stroking detection method and system that captures and processes images to extract feature symmetry indexes and blocking similarities, forming a determining feature set used by a classifier to identify stroking states or normal states, thereby increasing accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional detection methods using only expressional asymmetry and mouth alignment are used, then the detection system is simple, but the accuracy rate is low
Solution Approach 1:
The patent segments the facial detection process into multiple independent feature extraction modules: expressional asymmetry detection, mouth alignment detection, eyebrow position detection, and eye position detection. Each module extracts specific features independently, which are then combined to form a comprehensive determination. This segmentation allows the system to maintain simplicity while improving accuracy through multiple detection dimensions.
2Measurement precision
If multiple feature extraction modules are used to improve accuracy, then the detection accuracy improves, but the processing time increases
Solution Approach 1:
The patent implements a pre-processing step that standardizes the input image before feature extraction, including grayscale conversion, normalization, and key landmark point detection. This preliminary action prepares the data in advance, making subsequent feature extraction more efficient and reducing overall processing time while maintaining high accuracy through comprehensive feature analysis.
Data Source
AI summary
A facial stroking detection method includes a detecting step and a determining step. The detecting step includes a pre-processing step, a feature extracting step and a feature selecting step. In the pre-processing step, an image is captured by an image capturing device, and the image is pre-processed so as to obtain a post-processing image. In the feature extracting step, a plurality of image features are extracted from the post-processing image so as to form an image feature set. In the feature selecting step, a determining feature set is formed by selecting a part of the image features from the image feature set and entered into a classifier. In the determining step, wherein the classifier provides a determining result according to the determining feature set.