Image-Based Relationship Analysis Using Facial Recognition
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current social relationship analysis is limited by the need for data collection from multiple sources, which is time-consuming and costly, and often lacks accurate data transformation and analysis, particularly when using automated methods that neglect the significance of images in assessing relationships.
Innovation Solution
A computerized method and system for image-based relationship analysis using a Facial Recognition Model (FRM) to generate vector representations of individuals in images, cluster them, and create local and global relationship matrices to represent interactions, enabling the deduction of relationships from photographic data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is collected from multiple sources including communications and social media channels, then relationship analysis can be performed, but the process becomes time-consuming and costly
Solution Approach 1:
The patent extracts and focuses specifically on image data from the multitude of available communication sources. By isolating and utilizing only image representations from communications, the system eliminates the need to collect and process data from multiple unrelated sources, thereby reducing time and cost while maintaining relationship analysis capability
Solution Approach 2:
The patent makes image data serve multiple functions: it is used for both individual identification (through facial recognition) and relationship analysis (through co-occurrence patterns). This multi-functionality of image data replaces the need for separate data collection from communications and social media channels
2Loss of information
If data transformation and analysis are performed on multiple sources, then relationship insights can be obtained, but the process becomes costly and complex
Solution Approach 1:
The patent extracts only the necessary relationship information directly from image co-occurrence patterns, eliminating the need for complex data transformation processes required when integrating multiple data sources. The extraction focuses solely on identifying individuals and their spatial relationships in images
Solution Approach 2:
The patent uses facial recognition to create vector representations (copies) of individual identities from image data. These vector copies enable relationship analysis without requiring complex transformation of the original image data, simplifying the analysis process while preserving relationship information
3Productivity
If automated methods are used for relationship analysis, then efficiency is improved, but accuracy suffers due to neglecting image significance
Solution Approach 1:
The patent changes the parameters used for relationship measurement from traditional communication metrics (call counts, email frequencies) to image-based parameters (spatial distances, relative positions, co-occurrence frequencies in photographs). This parameter change enables automated processing while capturing relationship significance that was previously neglected
Solution Approach 2:
The patent introduces image data as an intermediary that bridges automated processing capability and relationship accuracy. Images serve as the mediating data type that can be automatically processed through facial recognition and spatial analysis while preserving the significance of real-world interactions that traditional communication data misses
Data Source
AI summary
There are provided a system and method of image-based relationship analysis, the method including: obtaining a set of target images each including one or more image representations of one or more individuals, obtaining, for each image representation, a corresponding vector representation, clustering the multiple vector representations to a plurality of clusters of vector representations corresponding to a plurality of unique individuals, and obtaining, for each target image, one or more unique individuals associated therewith, for each given target image of at least one subset of the set, obtaining a set of image parameters; generating a local relationship matrix using the set of image parameters, the local relationship matrix being representative of local mutual relationships between the one or more unique individuals, thereby obtaining a set of local relationship matrices, and generating a global relationship matrix by combining a set of local relationship matrices.