Distributed Facial Recognition for Cloud Robotics
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Solution Overview
Problem
Current face recognition systems in cloud robotics lack scalability and efficiency due to the limited computational power of individual robots and the high bandwidth requirements for image processing, making it costly to upgrade hardware and inefficient to perform complex computations on the robot side.
Innovation Solution
A distributed facial recognition system where a computerized robot captures images, applies initial face detection, and transmits processed images to a remotely located server for further processing, leveraging the server's greater computational power to perform facial recognition and interaction determination, thereby reducing the computational load on the robot and optimizing bandwidth usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If face recognition processing is performed on the robot side, then response time is reduced, but computational power requirements and hardware costs increase
Solution Approach 1:
The face recognition system is segmented into two parts: preliminary processing (image capture, initial face detection, and image transmission) is performed on the robot side, while complex computational tasks (facial recognition and interaction determination) are performed on the server side. This segmentation allows the robot to have simpler hardware while maintaining responsive interaction.
2Measurement precision
If complex image processing is performed on the robot, then recognition accuracy is improved, but bandwidth consumption increases
Solution Approach 1:
The system extracts and transmits only the essential face detection results and relevant image data to the server, rather than transmitting entire images or all processed data. This extraction approach maintains recognition accuracy while significantly reducing bandwidth consumption.
3Power
If hardware is upgraded to provide more computational power, then processing capability is improved, but system cost increases
Solution Approach 1:
A server acts as an intermediary between the robot and the face recognition database. The server provides the heavy computational power and storage resources needed for accurate face recognition, while the robot maintains its simpler hardware configuration. This intermediary approach allows the system to achieve high processing capability without upgrading expensive robot hardware.
Data Source
AI summary
A system and method for performing distributed recognition divides processing steps between a device, having lower processing power, and a remotely located server, having significantly more processing power. Images captured by the device are processed at the device by applying a first set of image processing steps that includes applying a first detection. First processed images having at least one detected human is transmitted to the server, whereas a second set of image processing steps are applied to determine a stored entry matching the detected human of the first processed image.