Facial Rejuvenation Scoring Using AI Key-Point Geometry
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Solution Overview
Problem
Existing methods for assessing aesthetic medicine effects are unreliable and lack the ability to provide quantitative data, leading to inconsistent evaluations and potential medical disputes due to manual comparison and limited facial recognition capabilities.
Innovation Solution
An antiaging assessment method and device using facial key points, spatial relationships, and artificial intelligence to quantify facial rejuvenation through feature change values and scoring, enabling precise and reliable evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual comparison and observation by professional doctors is used for aesthetic medicine assessment, then the assessment process is simple and quick, but the reliability and accuracy of the assessment deteriorates due to subjective judgment and lack of quantitative data
Solution Approach 1:
The patent replaces the manual mechanical assessment system with an automated image processing and AI analysis system. Facial recognition algorithms automatically detect and track facial key points across multiple images, compute spatial relationships, and generate quantitative assessment data, eliminating subjective manual judgment while maintaining operational simplicity through automated workflows
Solution Approach 2:
The patent transforms subjective visual assessment into objective quantitative parameters by measuring spatial distances, angles, and positional relationships between facial key points. The system calculates specific metrics such as the distance between eyebrow peaks and eye centers, angles of facial features, and relative positions of key landmarks, converting qualitative aesthetic evaluation into measurable data with defined reference ranges
2Measurement precision
If conventional facial recognition methods are used, then the assessment process is simplified, but the ability to grasp recognizability and variability of facial feature points deteriorates due to limited recognition capabilities
Solution Approach 1:
The patent segments the facial recognition task into distinct functional modules: facial key point detection, spatial relationship calculation, and assessment scoring. Each module handles a specific aspect of the analysis, with key point detection identifying 68 or more facial landmarks, spatial calculation computing distances and angles between these points, and scoring comparing measured values against reference ranges for different facial features
Solution Approach 2:
The patent introduces facial key points as intermediary elements that mediate between raw facial images and assessment results. These key points serve as measurable proxies for facial features, allowing the system to quantify spatial relationships and variations without directly analyzing complex facial structures. The key points act as intermediaries that translate visual facial characteristics into computable geometric parameters
3Productivity
If manual assessment methods are used, then the assessment system remains simple to operate, but the ability to combine treatment effects with big data analysis deteriorates due to lack of automated data processing
Solution Approach 1:
The patent implements continuous automated data processing that operates throughout the assessment workflow. The system continuously captures facial images at multiple time points, continuously computes spatial relationships between key points, and continuously compares results against reference data. This continuous automated processing eliminates idle periods and manual intervention, maintaining productive action throughout the entire assessment process from image capture to final scoring
Solution Approach 2:
The patent incorporates feedback mechanisms where assessment results are automatically compared against reference ranges and previous measurements. The system provides feedback by identifying which facial features fall outside normal ranges, highlighting specific areas requiring attention, and tracking changes over time. This automated feedback loop enables continuous improvement of treatment outcomes by providing actionable insights based on objective data rather than subjective judgment
Data Source
AI summary
An antiaging assessment method for facial rejuvenation is provided. Firstly, a spatial relationship between various facial key points of an under-test facial image are precisely described. Consequently, a facial feature is obtained. The spatial relationship is an angle relationship or a length relationship. Then, a feature change comparison between the facial feature and reference feature is performed. Consequently, a feature change value is obtained. According to the at least one feature change value, a facial rejuvenation score index is acquired from a corresponding feature change value range of a facial rejuvenation score index data table. The present invention also provides an antiaging assessment device for facial rejuvenation.