Facial Dimension Estimation from Unscaled Images
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Solution Overview
Problem
Current methods for estimating absolute facial dimensions for patient interfaces, such as masks for airway pressure therapy, are either time-consuming, error-prone, or require expensive calibrated optical scanners, and existing computer vision techniques cannot accurately determine absolute dimensions from unscaled image data.
Innovation Solution
A method that aligns and scales a facial model from image data to a reference average model, using statistical correlations between shape and size differences to estimate the absolute size dimensions, allowing for accurate reconstruction of facial dimensions from unscaled images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If calibrated optical scanners are used to measure absolute facial dimensions, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent creates a 3D facial model (copy) from 2D images that reproduces absolute facial dimensions without requiring physical contact or specialized scanning equipment. The model is generated through image processing algorithms that reconstruct three-dimensional geometry and scale from standard photographs, providing a cheaper alternative to optical scanners while maintaining measurement accuracy
Solution Approach 2:
The patent replaces the mechanical/optical scanning system with a computational image processing system. Instead of using calibrated optical scanners that require precise mechanical positioning and calibration, the invention uses algorithms to extract dimensional information from 2D images, substituting complex hardware with software-based solutions
2Device complexity
If manual measurement methods are used to obtain facial dimensions, then device complexity is reduced, but measurement precision and reliability deteriorate
Solution Approach 1:
The system automatically processes images and generates 3D facial models without requiring manual measurement input from users. The algorithm autonomously identifies facial landmarks, constructs the three-dimensional model, and extracts dimensional data, eliminating the need for operators to physically measure facial features while maintaining high precision
Solution Approach 2:
The patent replaces manual measurement processes with automated image processing algorithms. Instead of relying on operators to physically measure facial features with rulers or calipers, the system uses computational methods to extract dimensional information from images, eliminating human error and subjectivity
3Productivity
If computer vision techniques process images faster, then productivity is improved, but measurement precision of absolute dimensions deteriorates
Solution Approach 1:
The patent performs preliminary processing of the input images to enhance quality and extract key features before the main 3D reconstruction process. This includes image normalization, landmark detection, and feature extraction that prepare the data for accurate dimensional measurement, ensuring that speed optimizations do not compromise precision
Solution Approach 2:
The invention uses specialized image processing algorithms optimized for both speed and accuracy in extracting facial geometry. The computational methods are designed to efficiently process images while maintaining the precision needed for absolute dimensional measurements, replacing general-purpose computer vision techniques with domain-specific solutions
Data Source
AI summary
A method for estimating the absolute size dimensions of a test object based on image data of the test object, namely a face or part of a person. The method includes receiving image data of the test object, determining a first model of the test object based on the received image data, and aligning and scaling the first model to a first average model that includes an average of a plurality of first models of reference objects being faces or parts of faces of reference persons. The first models of the reference objects are of a same type as the first model of the test object. The method further includes determining a shape difference between the test object and an average of the reference objects, determining a second model of the test object with an estimated scale based on (i) the determined shape difference, (ii) a statistical operator that is indicative of a statistical correlation between shape and size dimensions of the reference objects, and (iii) a second average model, and determining the size dimensions of the test object based on the second model of the test object.