Low-Angle Camera Facial Expression Tracking
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Solution Overview
Problem
Wearable devices with cameras positioned close to or at low angles to the user cannot provide useful visual information, limiting their functionality in capturing facial expressions and eye movements for applications like avatar control.
Innovation Solution
Implementing low-angle cameras and eye-tracking cameras on wearable smart devices, combined with machine learning algorithms and depth sensors, to process images and derive valuable information for controlling avatars, even when cameras capture images nearly parallel to the user's face.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Volume of moving object
If cameras are positioned very close to and at low angles to the user, then the device can be made compact and wearable, but the images captured become nearly parallel to the user's face and lose useful visual information
Solution Approach 1:
The patent introduces an intermediary processing system that captures low-angle images and uses machine learning algorithms to reconstruct meaningful facial expression data. The intermediary software layer transforms the limited low-angle camera data into useful facial expression information, resolving the contradiction between compact camera positioning and information quality.
Solution Approach 2:
The system changes the parameter of image processing by applying machine learning models that learn to extract facial expression parameters from low-angle views. Instead of relying on traditional high-quality front-facing camera images, the system transforms the approach by learning to interpret distorted low-angle images and extract meaningful facial expression parameters.
2Measurement precision
If outward pointing cameras are used to capture useful views, then image quality improves, but the device cannot be positioned against the user's face for eye tracking and avatar control
Solution Approach 1:
The patent merges the functions of outward-pointing cameras and inward-pointing eye-tracking cameras into a unified system. By combining data from both camera types and processing them together through machine learning, the system achieves both accurate facial expression detection and reliable eye tracking, resolving the contradiction between these two functional requirements.
Solution Approach 2:
The wearable device achieves multi-functionality by using a single integrated camera system that serves multiple purposes: capturing facial expressions for avatar control, tracking eye movements, and providing visual information. The machine learning processing enables the same hardware to perform multiple functions that would traditionally require separate specialized components.
3Device complexity
If low-angle cameras capture images parallel to the user's face, then the device structure is simplified, but deriving valuable information becomes significantly more difficult
Solution Approach 1:
The patent replaces complex mechanical camera positioning with software-based intelligence. Instead of using multiple cameras positioned at various angles to capture facial expressions, the system uses a single low-angle camera combined with machine learning algorithms that simulate the computational work of multiple cameras, thereby simplifying the mechanical structure while maintaining detection capability.
Solution Approach 2:
The system performs preliminary action by training machine learning models in advance with large datasets of facial expressions captured from various angles. This pre-trained knowledge enables the system to quickly and accurately extract facial expression information from low-angle images during actual use, reducing the computational difficulty of real-time detection.
Data Source
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AI summary
The description relates to facial tracking. One example can include an orientation structure configured to position the wearable device relative to a user's face. The example can also include a camera secured by the orientation structure parallel to or at a low angle to the user's face to capture images across the user's face. The example can further include a processor configured to receive the images and to map the images to parameters associated with an avatar model.