Social Type Detection from Visual Content
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Solution Overview
Problem
There are no conventional tools that leverage visual content from social networks to characterize a user's social life, limiting the ability to gain knowledge about aspects of a person's social interactions and preferences.
Innovation Solution
A computer-implemented method that detects human faces in visual content, identifies age and gender, categorizes faces into buckets based on age and gender, and determines a social type using feature vectors, allowing for personalized content recommendations such as video advertisements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If visual content from social networks is analyzed to characterize user's social life, then knowledge about user's social interactions and preferences is gained, but no conventional tools exist to perform this characterization
Solution Approach 1:
The system segments visual content analysis into distinct processing stages: face detection, age estimation, gender classification, and social type determination. Each stage processes specific features independently and combines results to form a comprehensive user profile, making the complex analysis task manageable and systematic.
Solution Approach 2:
The system introduces intermediate processing layers between raw visual content and final social life characterization. Face detection algorithms serve as intermediaries to extract relevant features from images, which are then processed through classification models to generate meaningful social insights without requiring direct complex analysis of raw pixels.
2Measurement precision
If face detection and categorization is performed on visual content, then user's social type can be determined, but processing time and computational resources increase
Solution Approach 1:
The system performs partial analysis by focusing on key discriminative features (face presence, age group, gender) rather than analyzing all possible image attributes. This selective approach achieves sufficient precision for social type determination while significantly reducing processing time and computational overhead.
Solution Approach 2:
The system replaces complex manual or rule-based analysis mechanisms with automated machine learning models for face detection and classification. These models efficiently process visual content through learned patterns, achieving high accuracy faster than traditional image analysis methods would require.
Data Source
AI summary
Systems and methods to analyze a person's social media photos or videos, such as those posted on Twitter, Facebook, Instagram, etc. and determine properties of their social life. Using information on the number of people appearing in the photos or videos, their ages, and genders, this method can predict whether the person is in a romantic relationship, has a close family, is a group person, or is single. This information is valuable for generating audiovisual content recommendations as well as for advertisers, because it allows targeting personalized advertisements to the person posting the photos. The described methods may be performed (and the advertisements or other content may be selected for recommendation) substantially in real-time as the user accesses a specific online resource.