3D Face Model Generation for Pose-Invariant Recognition
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Solution Overview
Problem
Existing facial recognition systems face challenges in identifying individuals from images captured under unconstrained conditions due to variations in lighting, viewing angles, occlusions, and age, which complicates the process of sifting through large amounts of image data from diverse sources.
Innovation Solution
A method and system that determines a three-dimensional (3D) model of a face from multiple images, extracts two-dimensional (2D) patches, and generates multi-view probabilistic elastic parts (PEP) signatures, accounting for geometric and photometric variability, and incorporating attributes like gender, age, and ethnicity, to create an attribute-based representation that is pose-invariant and resilient to occlusions and low-resolution data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional 2D facial recognition methods are used on images from unconstrained conditions, then the system can process images quickly, but the recognition accuracy deteriorates due to variations in lighting, viewing angles, occlusions, and age
Solution Approach 1:
The patent transforms 2D facial images into a 3D model representation, adding a dimensional aspect to handle variations in viewing angle and lighting. The 3D model allows the system to normalize facial features across different poses and lighting conditions, thereby improving recognition accuracy in unconstrained conditions without sacrificing adaptability
Solution Approach 2:
The system changes the parameter space from 2D image coordinates to 3D geometric parameters and appearance parameters. By representing faces in 3D space with parameters for shape, texture, and lighting, the system can normalize variations caused by different viewing angles and lighting conditions, resolving the contradiction between accuracy and adaptability
2Reliability
If multiple images are processed to create comprehensive facial representations, then recognition robustness improves, but the time and computational resources required increase
Solution Approach 1:
The patent performs preliminary actions by constructing a 3D model from multiple images in advance, organizing facial data into a structured representation that captures geometric and appearance variations. This pre-processing creates a compact facial signature that can be quickly compared during recognition, thereby maintaining robustness while reducing real-time processing time
Solution Approach 2:
The system creates a simplified 3D model copy that captures the essential facial characteristics without storing all original images. This compact representation serves as a surrogate for the full set of images, maintaining recognition reliability while significantly reducing processing time and computational resources
3Adaptability or versatility
If 3D models are constructed from multiple images to handle pose variations, then pose invariance improves, but the system complexity increases
Solution Approach 1:
The patent segments the facial recognition system into distinct modules: image processing to extract facial features, 3D model construction to represent geometric structure, and signature generation for comparison. This segmentation allows each component to handle specific aspects of pose variation independently, achieving pose invariance while managing system complexity through modular design
Data Source
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AI summary
Methods and systems for facial recognition are provided. The method includes determining a three-dimensional (3D) model of a face of an individual based on different images of the individual. The method also includes extracting two-dimensional (2D) patches from the 3D model. Further, the method includes generating a plurality of signatures of the face using different combinations of the 2D patches, wherein the plurality of signatures correspond to respective views of the 3D model from different angles.