Structured Light Projection for Accurate Facial Feature Detection
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Solution Overview
Problem
Acquiring training data for machine learning models to detect facial features in HMD wearers is cumbersome and inaccurate due to the time-consuming and expensive process of manual annotation, and after-the-fact image analysis is inadequate in identifying reference points.
Innovation Solution
A method that recursively captures images of an object illuminated by projected structured light, identifies reference points, and modifies the structured light based on these points in each iteration to improve detection in subsequent images, allowing for more accurate identification of facial features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation is used to acquire training data for machine learning models, then the accuracy of reference point identification can be improved, but the time consumption and cost increase significantly
Solution Approach 1:
The system uses automated algorithms to identify reference points in facial images without requiring manual annotation. The machine learning model automatically detects and marks facial landmarks, enabling the system to acquire training data independently and efficiently, thus resolving the contradiction between accuracy and time consumption.
Solution Approach 2:
The patent replaces the mechanical process of manual annotation with an automated computational system. Instead of human operators manually marking reference points, the system uses computer vision algorithms and machine learning models to automatically identify and annotate facial features, significantly reducing time consumption while maintaining or improving accuracy.
2Extent of automation
If after-the-fact image analysis is used to identify reference points, then the process can be automated, but the accuracy of identifying reference points becomes inadequate
Solution Approach 1:
The system performs preliminary processing of facial images by projecting structured light patterns onto the face before capture. This preprocessing step enhances the visibility and detectability of facial features, enabling the subsequent automated analysis to achieve higher accuracy. The structured light illumination prepares the image data in advance, making reference points more easily identifiable by the automated algorithm.
Solution Approach 2:
The patent modifies the illumination parameters by using structured light patterns instead of conventional lighting. This change in lighting parameters enhances the contrast and definition of facial features in the captured images, thereby improving the accuracy of automated reference point identification while maintaining full automation of the process.
3Measurement precision
If structured light is projected onto the object to illuminate facial features, then the detection accuracy of facial features can be improved, but the complexity of the system increases
Solution Approach 1:
The system integrates the structured light projection device with the existing HMD and camera system, making the illumination component serve multiple functions. The same device that captures images also projects structured light patterns, eliminating the need for separate complex illumination systems and reducing overall system complexity while maintaining improved detection accuracy.
Data Source
AI summary
Structured light is projected onto an object, and an image of the object as illuminated by the projected structured light is captured. Reference object points are identified within the captured image, and the structured light projected onto the object is modified based on the identified reference object points. An additional image of the object as illuminated by the modified projected structured light is captured, and additional reference object points are identified within the captured additional image.