Facial Recognition Video Analytics Contextual Stream Identification
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Solution Overview
Problem
Existing systems face challenges in accurately analyzing and providing contextual video streams of individuals at public events, often requiring significant resources and failing to include desired participants, such as those taking pictures or videos, due to inefficiencies in facial recognition and video analytics.
Innovation Solution
An apparatus and method utilizing a processor with memory and communication interfaces to receive and analyze facial image data and video stream data through two-dimensional, three-dimensional facial recognition analytics, or convolutional neural nets, registering users and defining user-specific contextual video streams based on confidence levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional facial recognition and video analytics methods are used to identify individuals in video streams, then the system can provide basic identification functionality, but the analysis accuracy is insufficient and contextual data cannot be properly provided
Solution Approach 1:
The system segments the video stream analysis into multiple independent processing streams: facial feature extraction, contextual data analysis, and confidence level calculation. Each stream processes specific aspects separately and combines results to achieve both high identification accuracy and reliable contextual data provision
Solution Approach 2:
The patent introduces a new dimension of contextual data analysis alongside traditional facial recognition. By analyzing not only facial features but also surrounding context, events, and metadata, the system transforms single-dimensional identification into multi-dimensional verification, improving both accuracy and reliability
2Measurement precision
If comprehensive video stream analysis is performed to identify individuals and provide contextual data, then identification accuracy improves, but resource consumption increases significantly
Solution Approach 1:
The system performs partial analysis by focusing computational resources on key facial features and relevant contextual data only when needed. The confidence level mechanism determines whether full comprehensive analysis is necessary or if partial results suffice, reducing unnecessary resource consumption while maintaining accuracy when required
Solution Approach 2:
The system uses the confidence level metric to self-regulate resource allocation. When confidence is high, minimal processing is required; when confidence is low, the system automatically initiates more comprehensive analysis only for those specific cases, making the system efficient and adaptive to actual needs
3Productivity
If facial recognition data is collected and analyzed in real-time, then user identification can be provided, but the system fails to include desired participants such as those taking pictures or videos
Solution Approach 1:
The system is designed to be universal in its detection capabilities, analyzing not only faces but also objects, actions, and contextual elements in the video stream. This multi-functionality allows it to identify both the person being photographed and the person taking the photograph by analyzing different aspects of the same video data
Solution Approach 2:
The contextual data analysis provides feedback about the scene being captured, including information about cameras, photographers, and event context. This feedback loop allows the system to continuously refine its identification of desired participants by cross-referencing facial recognition results with contextual information about photo-taking activities
Data Source
AI summary
An apparatus includes a memory, a communication interface in communication with the memory and configured to communicate via a network, and a processor in communication with the memory and the communication interface. The processor receives facial image data associated with a user of a client device, registers the facial image data, and stores the facial image data and contextual data associated with the user in a database. The processor also receives video stream data from at least one image capture device in communication with the network, analyzes the video stream data and contextual data associated with the video stream data to define analyzed video data and analyzed contextual data, respectively, and defines a confidence level based on comparing the data associated with the video stream data to the data stored in the database. The processor defines a user-specific contextual video stream when the confidence level satisfies a criterion.