Face Attribute Classification via Reliability-Ordered Classifier Tree
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Solution Overview
Problem
Current systems face challenges in efficiently automating complex tasks such as face recognition and object identification in images due to computational bandwidth limitations, despite advances in distributed computing.
Innovation Solution
The method involves using multiple feature-extractors to generate feature vectors that are distributed among attribute classifiers, which are ordered by reliability to efficiently traverse the computational complexity, allowing for rapid and real-time processing of images to assign attributes like age, ethnicity, and gender to human face subimages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple feature-extractors and attribute classifiers are used to accurately identify face attributes, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system segments the face recognition task into multiple independent feature-extractors (e.g., age, gender, ethnicity detectors) and attribute classifiers. Each component processes specific features separately, allowing for modular design that improves measurement precision through specialized processing while managing complexity through functional decomposition.
Solution Approach 2:
The patent introduces a hierarchical dimension to the classification process by organizing attribute classifiers in a tree structure with different levels. This dimensional organization allows the system to process attributes in a structured sequence, improving accuracy through multi-level classification while managing computational complexity through hierarchical organization.
2Measurement precision
If comprehensive attribute classification is performed on all face subimages, then measurement precision is improved, but productivity decreases due to computational complexity
Solution Approach 1:
The system performs preliminary classification using higher-level attribute classifiers first to identify and filter face subimages. By pre-processing and categorizing images before applying more computationally intensive lower-level classification, the system achieves comprehensive attribute analysis while improving processing throughput through staged computation.
Solution Approach 2:
The patent applies partial classification action by selectively processing only those face subimages that meet certain criteria or require specific levels of analysis. Not all images undergo the complete classification pipeline, allowing the system to maintain high measurement precision for required cases while improving overall productivity by avoiding unnecessary processing of images that don't require full analysis.
Data Source
AI summary
The present document is directed to methods and systems that identify and characterize subimages in images that each includes an image of a human face. In certain implementations, values for attributes, such as age, ethnicity, and gender, are assigned to face-containing subimages by the currently disclosed methods and systems. In these implementations, multiple feature-extractors output feature vectors that are distributed among attribute classifiers which consist of individual classifiers and, more often, multiple individual classifiers within aggregate classifiers. Attribute classifiers return indications of attribute values along with a probability value. Attribute classifiers are ordered with respect to reliability and applied in reliability order to generate attribute-assignment paths through a logical attribute-assignment tree, with uncertain attribute assignments generating multiple lower-level pathways.