Facial Expression Determination Using Segmented Feature Point Analysis
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Solution Overview
Problem
There is a need for an apparatus and method to determine facial expression types by analyzing facial images, which can provide personalized healthcare services and health monitoring, especially in the context of increasing health consciousness and diversified consumer demands.
Innovation Solution
A method and apparatus that extract feature points from facial images, group them into regions, and use a facial expression type function to determine and represent facial expressions, considering age information and classifying shapes such as face, eye, and nose shapes into specific criteria, generating a facial expression type function based on normal distribution and preset criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If facial images of multiple users are collected and analyzed to generate a facial expression type function, then the accuracy and precision of facial expression determination is improved, but the complexity of the system and the amount of data processing required increases
Solution Approach 1:
The patent divides the facial image analysis into multiple independent steps: extracting feature points, grouping them into regions (face, eye, forehead, nose), and determining expression types for each group separately. This segmentation reduces system complexity by breaking down the complex task of facial expression analysis into manageable modules, while still achieving high accuracy through comprehensive analysis of multiple facial regions.
2Adaptability or versatility
If age information is incorporated to calibrate the facial expression type function, then the personalization and accuracy for different age groups is improved, but the complexity of calibration and data processing increases
Solution Approach 1:
The patent applies local quality by calibrating the facial expression type function specifically for different age groups rather than using a universal model. Age information is used to adjust the calibration parameters locally for each age group, improving personalization and accuracy for specific demographics while managing complexity through targeted rather than universal calibration approaches.
3Reliability
If multiple feature points are extracted and grouped into multiple regions for analysis, then the comprehensiveness of facial expression analysis is improved, but the processing time and computational load increase
Solution Approach 1:
The patent segments facial feature points into distinct regions (face, eye, forehead, nose) and processes each region independently to determine expression types. This segmentation allows parallel processing of different facial regions, reducing overall processing time while maintaining comprehensive analysis through the inclusion of multiple facial regions. The modular approach enables efficient computation without sacrificing analytical thoroughness.
Data Source
AI summary
A method of determining a facial expression type may include extracting feature points representing facial characteristics from a facial image of a user; grouping the extracted feature points into a plurality of regions; and determining a facial expression type for each of a plurality of groups using feature points and a facial expression type function corresponding to each of the groups.