Face Recognition Using 3D Model Synthesis for Pose Handling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Digital image recognition systems face challenges in accurately identifying objects and faces due to variations in pose and occlusion, leading to degraded performance when objects are tilted or partially occluded, as they often rely on simplifying assumptions about frontal views.
Innovation Solution
The system generates synthesized images to compensate for pose and occlusion by using three-dimensional models to reorient and reposition objects, allowing for recognition of objects in non-frontal views, and adaptively selects and updates training sets based on recognition performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If the system uses simplifying assumptions about frontal views for recognition, then the device complexity is reduced, but the recognition accuracy deteriorates when objects are tilted or occluded
Solution Approach 1:
The patent transitions from 2D image analysis to 3D modeling by creating three-dimensional representations of objects. This dimensional elevation allows the system to handle objects at arbitrary viewpoints and occlusions by reasoning about their three-dimensional structure rather than being constrained to frontal 2D views, thereby improving recognition accuracy without proportionally increasing system complexity
Solution Approach 2:
The system creates synthesized copies of objects from 3D models to generate virtual views that match the observed perspective. By copying and transforming 3D models to simulate different viewpoints and lighting conditions, the system can compare these synthesized copies with actual images to improve recognition accuracy while maintaining manageable complexity through algorithmic generation rather than extensive data collection
2Adaptability or versatility
If the system handles objects at arbitrary viewpoints and occluded regions, then the adaptability is improved, but the device complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-establishing 3D models of objects before recognition tasks. These 3D models serve as prepared templates that can be quickly transformed and compared against images with arbitrary viewpoints or occlusions. This preliminary modeling phase enables the system to handle diverse viewing conditions without requiring complex real-time processing for each specific case
Solution Approach 2:
The system changes parameters by transforming 3D models through various operations including rotation, scaling, and projection to match different viewing conditions. By dynamically adjusting these transformation parameters based on the observed image characteristics, the system achieves high adaptability to arbitrary viewpoints and occlusions while maintaining a unified recognition framework that doesn't proportionally increase complexity
Data Source
AI summary
Embodiments described herein facilitate or enhance the implementation of image recognition processes which can perform recognition on images to identify objects and/or faces by class or by people.