Facial Component Segmentation for Demographic Classification
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Solution Overview
Problem
Existing methods for automatic extraction of demographic information from images, such as age, gender, and ethnicity, are limited as they rely on full-face images and do not utilize facial components for classification, making them ineffective for fusion of classifier results and applicable only to specific demographic classifications.
Innovation Solution
A system and method that employs a face detector module to identify faces, followed by a component detection module to extract features from facial components, which are then fed into classifiers using data level or hierarchical fusion models to accurately determine demographic information, allowing for improved accuracy through serial, parallel, or hybrid organization of classifiers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full-face images are used for demographic classification, then the system can process images without component detection, but the accuracy and applicability to multiple demographic categories are limited
Solution Approach 1:
The patent segments the face image into multiple facial components (eyes, eyebrows, nose, mouth, etc.) and performs classification on each component separately. This segmentation allows the system to capture localized demographic features that are missed in full-face analysis, thereby improving classification accuracy while enabling multi-demographic classification through component-level feature extraction.
2Adaptability or versatility
If facial components are used for classification, then accuracy and multi-demographic classification are improved, but the system complexity increases due to component detection and fusion mechanisms
Solution Approach 1:
The patent divides the classification task into multiple independent classifiers, each dedicated to a specific demographic category (age, gender, ethnicity). Each classifier processes component features independently, which simplifies the fusion mechanism while improving versatility across multiple demographic categories.
Solution Approach 2:
The patent creates a universal component detection framework that serves multiple classification purposes simultaneously. The same detected facial components are fed into multiple demographic classifiers, allowing the system to perform age, gender, and ethnicity classification using a single component detection pipeline, thereby improving versatility without proportionally increasing complexity.
3Loss of information
If component detection is implemented, then demographic information extraction is enhanced, but the processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary detection and localization of facial components before classification. By pre-identifying and cropping relevant facial components, the system reduces the amount of data that needs to be processed during classification, thereby minimizing information loss while reducing overall processing time compared to analyzing entire face images.
Data Source
AI summary
The present invention includes a system and method for automatically extracting the demographic information from images. The system detects the face in an image, locates different components, extracts component features, and then classifies the components to identify the age, gender, or ethnicity of the person(s) in the image. Using components for demographic classification gives better results as compared to currently known techniques. Moreover, the described system and technique can be used to extract demographic information in more robust manner than currently known methods, in environments where high degree of variability in size, shape, color, texture, pose, and occlusion exists. This invention also performs classifier fusion using Data Level fusion and Multi-level classification for fusing results of various component demographic classifiers. Besides use as an automated data collection system wherein given the necessary facial information as the data, the demographic category of the person is determined automatically, the system could also be used for targeting of the advertisements, surveillance, human computer interaction, security enhancements, immersive computer games and improving user interfaces based on demographic information.