Face Extraction and Grouping for Personalized Image Sorting
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Solution Overview
Problem
Existing image management systems struggle to provide personalized sorting and searching of images based on user environment and tendencies, as they primarily rely on face detection and attribute analysis without considering user-specific context.
Innovation Solution
An information processing device and method that extracts faces from images, classifies them into groups, adds tags indicating relationships between persons, calculates closeness between groups, and generates person correlation data, enabling personalized image sorting and searching based on user environment and tendencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If face detection and attribute analysis are used for image classification, then image sorting capability is improved, but personalization according to user environment and tendencies deteriorates
Solution Approach 1:
The system establishes feedback loops where user interactions with sorted images (selections, modifications, rejections) are continuously analyzed. The classification results are fed back to update user profiles and refine sorting algorithms, enabling the system to adapt to individual user preferences and behaviors over time.
Solution Approach 2:
The patent implements dynamic classification criteria that adjust based on user context. Sorting parameters such as time of day, user location, recent activity patterns, and preferred categories are continuously updated. The system transitions from static attribute-based sorting to dynamic context-aware sorting that evolves with user behavior.
2Measurement precision
If detailed attribute information is detected and classified, then image classification precision is improved, but system complexity deteriorates
Solution Approach 1:
The patent divides the complex classification task into multiple independent modules: face detection module, attribute analysis module, context extraction module, and sorting execution module. Each module handles specific aspects of classification independently, reducing overall system complexity while maintaining comprehensive classification capabilities.
Solution Approach 2:
The system introduces intermediate data structures and processing layers between raw image data and final classification results. Attribute information is first extracted into structured formats, then enriched with contextual data, and finally processed by sorting algorithms. This intermediary processing simplifies the overall system architecture.
Data Source
AI summary
An information processing device includes: an extracting unit configured to extract a face from an image; a grouping unit configured to classify the image extracted by the extracting unit into a group; an adding unit configured to add to a group generated by the grouping unit a tag indicating relationship between persons within the group; a closeness calculating unit configured to calculate the closeness of the person of the face from distance between groups generated by the grouping unit; and a person correlation data generating unit configured to generate person correlation data including at least the tag and the closeness.