Radar-Based Facial Tracking Using Machine Learning and Reflectors
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Solution Overview
Problem
Current facial tracking systems, particularly in augmented reality (AR) contexts, face challenges with cameras requiring high power consumption, significant space, and inability to track movements through facial hair, masks, or in low-light conditions, while radar sensors offer low spatial resolution and require complex processing methods suited for object detection rather than relative pose estimation.
Innovation Solution
The implementation of radar-based tracking systems that translate radar data into facial landmarks using depth, displacement, and velocity information, combined with machine learning models trained on camera data to customize facial tracking for individual users, and the use of metallic reflectors to broaden the field of view and improve sensing accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If camera-based tracking systems are used, then facial tracking can be performed, but power consumption is high and spatial requirements are significant
Solution Approach 1:
The patent replaces camera-based optical tracking with radar-based electromagnetic wave tracking. Radar sensors emit electromagnetic waves that penetrate facial hair and masks, enabling tracking without visible light. This substitution reduces power consumption while maintaining tracking functionality through depth, displacement, and velocity measurements.
Solution Approach 2:
The system transitions from optical parameter measurement (camera images) to electromagnetic wave parameter measurement (radar signals). By measuring depth, displacement, and velocity through electromagnetic wave reflection and phase changes, the system achieves accurate facial tracking with lower power consumption and improved penetration through occlusions.
2Reliability
If camera-based tracking systems are used, then facial tracking can be performed, but the system cannot track through facial hair, masks, or in low-light conditions
Solution Approach 1:
The patent replaces camera-based optical tracking with radar-based electromagnetic wave tracking. Radar sensors emit electromagnetic waves that penetrate facial hair and masks, enabling tracking without visible light. This substitution reduces power consumption while maintaining tracking functionality through depth, displacement, and velocity measurements.
Solution Approach 2:
The system transitions from optical parameter measurement (camera images) to electromagnetic wave parameter measurement (radar signals). By measuring depth, displacement, and velocity through electromagnetic wave reflection and phase changes, the system achieves accurate facial tracking with lower power consumption and improved penetration through occlusions.
3Use of energy by moving object
If radar sensors are used, then power consumption is reduced and penetration through hair and masks is improved, but spatial resolution is low and complex processing is required
Solution Approach 1:
The patent applies machine learning models that are pre-trained on camera data to process radar measurements. This preliminary training enables the system to translate radar depth, displacement, and velocity information into accurate facial landmark positions, compensating for radar's lower spatial resolution and simplifying the processing complexity.
Solution Approach 2:
The system uses machine learning models as an intermediary between radar sensors and facial tracking output. The models translate radar electromagnetic wave measurements into meaningful facial landmark positions, bridging the gap between radar's lower spatial resolution and the high precision required for facial tracking.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables precise and accurate facial tracking that overcomes the limitations of camera-based systems, including occlusions and low-light conditions, with reduced power consumption and spatial requirements, while leveraging radar sensors' ability to penetrate hair and masks.
Implementation Method 1
a radar component configured to: (1) emit one or more radar signals and (2) analyze one or more return signals (e.g., by identifying one or more features of an object toward which the radar signals were directed)
Implementation Method 2
analyze one or more return signals (e.g., by identifying one or more features of an object toward which the radar signals were directed)
Implementation Method 3
the use of metallic reflectors to broaden the field of view and improve sensing accuracy
Data Source
AI summary
A computer-implemented method may include (1) causing a radar component to emit one or more radar signals and (2) causing the radar component to analyze one or more return signals. Also disclosed is a method for forming a 3D liquid crystal polarization hologram optical element and a method for characterizing diffractive waveguides includes directing light onto a structure and measuring the diffracted light to capture at least one image of the structure. Lastly, disclosed is a method of pattering organic solid crystals and a method directing a beam of input light to a surface of an optical material to determine crystallographic and optical parameters of the optical material. Various other methods, systems, and computer-readable media are also disclosed.