Media Playback Face Recognition With Local Frame Analysis
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Solution Overview
Problem
Existing video playback devices face challenges in identifying individuals in videos due to slow and expensive cloud-based facial recognition processes that may also be inaccessible.
Innovation Solution
Implementing an edge-based face recognition process on media devices using systems on a chip (SoC) for local processing, including face detection and recognition technologies, which analyze video frames locally to reduce remote work and improve efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If cloud-based facial recognition service is used, then facial recognition functionality is provided, but processing speed is slow and cost is high
Solution Approach 1:
The patent extracts the facial recognition processing capability from the cloud and implements it locally on the media device using an edge computing platform. The system includes a processor that can execute facial recognition algorithms locally, eliminating the need to upload videos to cloud servers and thereby dramatically reducing processing time and cost while maintaining recognition functionality.
Solution Approach 2:
The patent introduces an edge computing platform as an intermediary between the media device and cloud services. This intermediary enables local processing of facial recognition tasks, allowing the device to perform analysis independently without relying on remote servers, thus resolving the speed and accessibility issues of cloud-based approaches.
2Ease of operation
If cloud-based facial recognition service is used, then facial recognition functionality is provided, but accessibility is limited
Solution Approach 1:
The patent enables the media device to perform facial recognition independently using its own processor and edge computing capabilities. The device no longer depends on external cloud services for this functionality, making it self-sufficient and accessible even when network connectivity is unavailable or services are inaccessible.
3Productivity
If video is uploaded to cloud for analysis, then facial recognition is performed, but bandwidth usage increases
Solution Approach 1:
The patent extracts the processing task from the cloud and executes it locally on the media device. By implementing facial recognition algorithms on the device's processor, the system eliminates the need to upload video files to remote servers, thereby consuming zero bandwidth for this purpose while maintaining full recognition capability.
4Power
If cloud servers are used for processing, then computational power is available, but computational load on servers increases
Solution Approach 1:
The patent extracts the computational workload from cloud servers and relocates it to the media device's local processor. This redistribution eliminates the need for servers to process facial recognition tasks, thereby reducing server energy consumption and computational load while providing the necessary processing power locally on the device.
Data Source
AI summary
A method includes accessing a video comprising a set of frames; receiving a command to identify a target displayed in a first frame; generating first target data based on the target; generating a first confidence level between the first target data and second target data, wherein the second target data comprises identity data; in response to the first confidence level being at or above a confidence level threshold, outputting the identity data of the second target data; otherwise, generating third target data based on the target displayed in a second frame of the set of frames; generating a second confidence level of similarity between the third target data and the second target data stored in the remote database; in response to the second confidence level being at or above the confidence level threshold, outputting the identity data of the second target data.