2D Camera Face Distance Estimation Without Depth Sensors
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Solution Overview
Problem
Existing compute devices face increased complexity, cost, and power consumption when incorporating depth sensors for estimating human face distance, necessitating a more efficient method using existing cameras and anatomical facial information.
Innovation Solution
Estimate human face distance using 2D image data from a device's camera, combined with known camera characteristics and anatomical facial information, employing geometric properties to determine the distance without complex depth estimation algorithms, focusing on a finite number of facial landmarks for reduced computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If depth sensors are incorporated into compute devices for estimating human face distance, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent extracts the distance estimation functionality from dedicated depth sensors and implements it using the existing camera system. By taking out the depth sensing capability from a separate hardware component and embedding it in the camera software processing, the patent maintains measurement precision while reducing device complexity.
Solution Approach 2:
The patent makes the camera system multi-functional by enabling it to perform both standard image capture and depth estimation tasks. Through anatomical-based distance estimation algorithms, the camera serves dual purposes: capturing visual data and measuring distance, thereby eliminating the need for separate depth sensors and reducing overall device complexity.
2Measurement precision
If depth sensors are incorporated into compute devices for estimating human face distance, then measurement precision is improved, but manufacturing cost increases
Solution Approach 1:
The patent replaces expensive depth sensing hardware with a software-based solution running on the existing camera. This approach uses inexpensive computational resources and algorithms instead of costly specialized sensors, significantly reducing manufacturing costs while maintaining acceptable measurement precision for human-machine interface applications.
Solution Approach 2:
The patent changes the operational parameters of the existing camera system to enable distance estimation. By adjusting focal length parameters, incorporating anatomical facial measurements, and modifying image processing algorithms, the system achieves depth sensing capabilities without requiring new hardware components, thereby reducing manufacturing expenses.
3Measurement precision
If depth sensors are incorporated into compute devices for estimating human face distance, then measurement precision is improved, but power consumption increases
Solution Approach 1:
The patent enables the existing camera system to self-serve multiple functions by integrating distance estimation capabilities into its standard operation. The camera processes images to extract both visual and depth information simultaneously, eliminating the need for separate power-hungry depth sensors and reducing overall power consumption while maintaining measurement precision.
Solution Approach 2:
The patent merges the distance estimation function with the camera's image capture process. By combining these operations into a single processing pipeline that uses anatomical facial features, the system achieves depth sensing without requiring additional active sensors, thereby reducing power consumption while preserving measurement accuracy.
4Measurement precision
If complex depth estimation algorithms are used, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the complex depth estimation problem into manageable components: detecting facial landmarks, measuring distances between specific anatomical points, and applying geometric calculations. This segmentation simplifies the overall algorithm while maintaining precision by focusing on key anatomical features rather than processing entire images with complex algorithms.
Solution Approach 2:
The patent applies local quality by focusing computational efforts on specific anatomical regions of the face rather than processing the entire image globally. By identifying and measuring distances between key facial landmarks (eyes, nose, mouth), the system achieves accurate distance estimation with reduced computational complexity compared to whole-face or whole-scene analysis.
Data Source
AI summary
Systems, apparatus, articles of manufacture, and methods are disclosed to perform human face distance estimation using a single calibrated two-dimensional (2D) camera, thereby eliminating the need for stereo depth systems, time-of-flight cameras, infrared pattern projectors, etc. Disclosed example techniques estimate three-dimensional (3D) distance from a camera to a human face by combining 2D facial landmarks, corresponding reconstructed 3D landmarks, and known anatomical distance priors such as interpupillary distance. An example multi-landmark technique that addresses occlusion issues by computing weighted-average distance estimates from multiple facial feature pairs is also disclosed. The example human face distance estimation techniques disclosed herein enable implementation of human-machine interfaces based on human face distance using standard 2D cameras with minimal software complexity.