Face Recognition Metadata Assignment for Personal Image Collections
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Solution Overview
Problem
Users face challenges in organizing and indexing large personal image collections due to unreliable timestamps and the need for significant manual input to apply metadata, especially when images are scattered across various devices and platforms.
Innovation Solution
A computer-implemented method that leverages metadata from social network applications to automatically assign tags to images in personal collections by training a face recognition algorithm using tagged images from social networks, allowing for efficient indexing and retrieval of images based on identified individuals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If timestamps are automatically assigned to images by capturing devices, then the indexing process is automated, but the reliability of the metadata is poor due to incorrect clock settings and irrelevant transfer dates
Solution Approach 1:
The system performs preliminary actions by having users manually tag a subset of images with correct metadata (people, events, locations) before using this tagged data to automatically annotate the entire collection. This preliminary manual tagging establishes a reliable foundation for subsequent automated processing.
Solution Approach 2:
The patent introduces an intermediary process that uses visually categorized faces and social connections as a mediator between unreliable automatic timestamps and the need for accurate metadata. The system leverages face recognition and social network relationships to bridge the gap between automated processing and reliable information.
2Reliability
If manual visual categorization is used to apply metadata to images, then the accuracy of metadata is improved, but the time and effort required for processing increases significantly
Solution Approach 1:
The system applies partial action by requiring users to manually tag only a subset of images rather than the entire collection. This partial manual effort is then leveraged to automatically annotate all remaining images, significantly reducing the total time investment while maintaining high accuracy.
Solution Approach 2:
The system enables self-service by using the manually tagged subset to automatically generate metadata for the entire collection. Once users provide initial training data, the system autonomously performs the remaining metadata assignment without requiring continuous manual input.
3Adaptability or versatility
If images are distributed across multiple devices and platforms, then users maintain private collections, but the complexity of managing and indexing these images increases
Solution Approach 1:
The system achieves universality by implementing a platform-agnostic face recognition and metadata assignment mechanism that works across multiple devices and storage locations. The solution provides multi-functional capability to handle images regardless of their source or storage location, unifying the management of distributed collections.
Solution Approach 2:
The patent uses copying by creating visual models of faces from images on various platforms and using these models to identify and tag the same individuals across the entire distributed collection. This copying approach allows consistent metadata assignment across multiple devices without requiring centralized storage.
Data Source
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AI summary
Various technologies pertaining to assigning metadata to images in a personal image collection of a user based upon images and associated metadata assigned thereto that are accessible to the user by way of a social network application are described. An account of the user in a social network application is accessed to retrieve images and metadata that is accessible to the user. A face recognition algorithm is trained based upon the retrieved images and metadata, and the trained face recognition algorithm is executed over the personal image collection of the user, where the personal image collection of the user is external to the social network application.