Dynamic Speckle Pattern Illumination for Depth Mapping
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Solution Overview
Problem
Traditional systems for facial recognition and face detection using speckle pattern illumination are time-consuming and power-intensive, especially for mobile devices, as they require capturing and analyzing both sparse and dense speckle pattern images to determine the suitable illumination for depth mapping.
Innovation Solution
A method and device that dynamically control the density of the speckle pattern illumination by segmenting the VCSEL array and adjusting the number of active emitters, allowing for either sparse or dense pattern projection based on the distance of the subject from the camera, thereby optimizing illumination for efficient depth mapping and power usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If both sparse and dense speckle pattern images are captured and analyzed to determine suitable illumination, then illumination selection accuracy is improved, but processing time and power consumption increase
Solution Approach 1:
The system performs preliminary action by capturing a reference image before capturing depth images, and uses this reference image to determine the appropriate speckle pattern density. This preliminary assessment allows the system to select the optimal illumination pattern in advance, avoiding the need to capture and analyze both sparse and dense patterns before making a decision, thus reducing processing time while maintaining accurate illumination selection
Solution Approach 2:
The system uses the reference image itself to determine the subject distance and select the appropriate speckle pattern density, rather than requiring external analysis of multiple depth images. The reference image provides sufficient information for the system to self-determine the optimal illumination configuration, eliminating the need for redundant image capture and analysis
2Reliability
If both sparse and dense speckle pattern images are captured and analyzed to determine suitable illumination, then illumination selection accuracy is improved, but power consumption increases
Solution Approach 1:
The system performs preliminary action by capturing a reference image before capturing depth images, and uses this reference image to determine the appropriate speckle pattern density. This preliminary assessment allows the system to select the optimal illumination pattern in advance, avoiding the need to capture and analyze both sparse and dense patterns before making a decision, thus reducing processing time while maintaining accurate illumination selection
Solution Approach 2:
The system uses the reference image itself to determine the subject distance and select the appropriate speckle pattern density, rather than requiring external analysis of multiple depth images. The reference image provides sufficient information for the system to self-determine the optimal illumination configuration, eliminating the need for redundant image capture and analysis
3Measurement precision
If dense speckle pattern illumination is used, then depth image resolution is improved, but power consumption and processing load increase
Solution Approach 1:
The system applies local quality by selecting different speckle pattern densities based on the subject distance. For close subjects, sparse patterns are used to prevent speckle overlap, while for distant subjects, dense patterns provide higher resolution. This localized adaptation of pattern density ensures optimal measurement precision for each distance scenario while minimizing unnecessary power consumption from using dense patterns when not required
4Measurement precision
If dense speckle pattern illumination is used, then depth image resolution is improved, but processing complexity increases
Solution Approach 1:
The system applies local quality by selecting different speckle pattern densities based on the subject distance. For close subjects, sparse patterns are used to prevent speckle overlap, while for distant subjects, dense patterns provide higher resolution. This localized adaptation of pattern density ensures optimal measurement precision for each distance scenario while minimizing unnecessary power consumption from using dense patterns when not required
Data Source
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AI summary
An estimate of distance between a user and a camera on a device is used to determine an illumination pattern density used for speckle pattern illumination of the user in subsequent images. The distance may be estimated using an image captured when the user is illuminated with flood infrared illumination. Either a sparse speckle (dot) pattern illumination pattern or a dense speckle pattern illumination pattern is used depending on the distance between the user's face and the camera.