Object Recognition Using Segmented Image and Depth Cameras
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Solution Overview
Problem
Current biometric authentication methods for mobile devices, such as RFID cards, fingerprint readers, and 2D imaging systems, are vulnerable to unauthorized use and spoofing, particularly due to the limitations of two-dimensional sensors and the size and cost constraints of three-dimensional imaging systems in mobile phones.
Innovation Solution
An apparatus comprising a high-resolution image camera and a low-resolution depth map camera, where the image camera captures a first resolution image and the depth map camera captures a second resolution depth map, allowing for efficient object recognition by determining whether a detected object is two-dimensional or three-dimensional, and integrating processing directly into the cameras to reduce spoofing risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a three-dimensional imaging system is used to prevent spoofing, then security is improved, but device size and cost increase
Solution Approach 1:
The system divides the imaging function into two separate cameras: one dedicated to capturing depth information and another dedicated to capturing color information. This segmentation allows each camera to be optimized for its specific function, with the depth camera being simpler and smaller, thus reducing overall device size while maintaining 3D authentication capability.
Solution Approach 2:
The depth mapping functionality is extracted from the main imaging system and implemented as a separate, dedicated camera module. This extraction allows the main camera to focus on high-quality color imaging while the separate depth camera provides the necessary 3D information, enabling reduced complexity and size compared to a monolithic 3D imaging system.
2Reliability
If a three-dimensional imaging system is used to prevent spoofing, then security is improved, but manufacturing cost increases
Solution Approach 1:
By segmenting the imaging system into separate depth and color cameras, each component can be manufactured independently using standard camera fabrication processes. This segmentation simplifies manufacturing compared to creating a single complex 3D imaging system, as each camera module can be produced using established industrial processes and then assembled together.
3Measurement precision
If a high-resolution depth map camera is used, then object recognition accuracy is improved, but device size increases
Solution Approach 1:
The system applies partial action by using a lower-resolution depth camera than would be ideal for maximum precision. This compromise accepts that the depth camera does not need to be as high-resolution as the color camera, since depth information is supplementary rather than primary. This partial action approach reduces device size while providing sufficient depth data for secure authentication.
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 enhances the security and efficiency of object recognition, reducing the size and cost of the authentication system while providing robust protection against unauthorized access by distinguishing between 2D and 3D objects.
Implementation Method 1
an image camera to capture a first resolution image
Implementation Method 2
a depth map camera to capture a second resolution depth map
Data Source
AI summary
An apparatus for performing object recognition includes an image camera to capture a first resolution image and a depth map camera to capture a second resolution depth map. The first resolution is greater than the second resolution. The apparatus is configured to perform object recognition based on the image and the depth map.