Whole-Image Embedding for Privacy-Preserving Visual Content Identification
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Solution Overview
Problem
Existing facial recognition systems violate privacy by processing images without consent, leading to potential violations of biometric regulations and inefficiencies in handling multiple individuals.
Innovation Solution
A system that processes entire images using whole-image embedding representations (WIER) trained on general image understanding tasks, preserving privacy by not isolating individuals and using methods that are not face-specific, allowing for efficient identification of multiple people in an image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If facial recognition systems process images to identify individuals, then identification accuracy is improved, but privacy protection deteriorates due to processing without consent
Solution Approach 1:
The patent extracts only the necessary identification information from images while leaving out sensitive personal data. The system processes images to detect and recognize individuals but does not store or process the actual facial images or personal identifiable information, thus achieving identification accuracy while protecting privacy.
Solution Approach 2:
The patent introduces an intermediary processing layer that acts as a mediator between the image input and the identification output. This intermediary system uses automated recognition algorithms to identify individuals without direct human involvement in processing their biometric data, thereby maintaining accuracy while reducing privacy harm through automated, consent-based processing.
2Measurement precision
If facial recognition systems process each individual separately, then identification accuracy is improved, but processing efficiency deteriorates when handling multiple people
Solution Approach 1:
The patent merges the processing of multiple individuals into a single unified processing pipeline. Instead of handling each person separately through multiple independent processing chains, the system processes all detected individuals in parallel within one processing framework, maintaining identification accuracy while significantly improving processing efficiency for images containing multiple people.
3Reliability
If facial recognition systems store biometric data for identification, then recognition reliability is improved, but security risks worsen due to potential data breaches
Solution Approach 1:
The patent extracts and processes only the minimum necessary biometric information required for identification purposes. By extracting only essential features needed for recognition while excluding unnecessary personal data, the system maintains recognition reliability while minimizing the security risks associated with storing and protecting large volumes of sensitive biometric information.
Data Source
AI summary
The system receives data indicating an individual and processes the data to isolate the individual and to enhance data quality. The system extracts a first multiplicity of key features of the data, which tend to uniquely identify the individual. The system compares, using artificial intelligence, the first multiplicity of key features associated with the data to a second multiplicity of key features associated with a user to determine whether the data indicates the user. Upon determining that the data indicates the user, the system retrieves from a datastore a rule associated with the second multiplicity of key features and determines whether the rule permits use of the data indicating the individual. Upon determining that the rule associated with the second multiplicity of key features does not permit use of the data indicating the individual, the system sends an indication that the rule does not permit the use.