3D Face Recognition Using Weighted Geometric Feature Analysis
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Solution Overview
Problem
Current two-dimensional face recognition methods suffer from inaccuracies due to varying 3-D feature attributes at neighboring locations, leading to significant errors in selecting facial feature points for recognition.
Innovation Solution
A three-dimensional face recognition system that analyzes geometric characteristics such as relative location, curvature, and normal vectors to select feature points, assigning different weight ratios based on their significance, and uses 2-D facial image data to confirm and enhance the selection of outstanding feature points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If two-dimensional face recognition methods are used, then the system is simple and fast, but the recognition accuracy deteriorates due to varying 3-D feature attributes at neighboring locations
Solution Approach 1:
The patent transitions from two-dimensional face recognition to three-dimensional face recognition by introducing depth information through 3-D face models. This dimensionality change allows the system to capture geometric characteristics (curvature, normal vectors, relative locations) that are invariant to facial expressions, thereby improving recognition accuracy while maintaining system manageability through structured 3-D model representation
2Measurement precision
If 3-D face models are used with detailed geometric analysis, then the feature point selection accuracy is improved, but the processing time increases
Solution Approach 1:
The patent performs preliminary analysis of geometric characteristics (curvature, normal vectors, relative locations) during the 3-D face model construction phase. By pre-processing and storing these geometric features in the 3-D face model database, the system avoids repeated complex calculations during recognition, thus improving feature point selection accuracy while minimizing processing time during actual recognition operations
3Reliability
If feature points are selected based on geometric characteristics, then the recognition robustness against facial expressions is improved, but the complexity of feature point selection increases
Solution Approach 1:
The patent changes the selection criteria for feature points from appearance-based 2-D features to geometry-based 3-D parameters including curvature, normal vectors, and relative locations. These geometric parameters are inherently more robust to facial expressions and lighting changes. The system manages the increased complexity by systematically analyzing these geometric characteristics during 3-D model construction and using weighted comparison methods during recognition
Data Source
AI summary
A 3-D face recognition system has a first data storing module for storing 3-D face model data and 2-D face image data; an input unit for inputting 3-D face model data and 2-D face image data; a signal converting module for converting analog data of the 3-D face model data and 2-D face image data to digital data; a second data storing module for storing the digital data; a microprocessing module for analyzing geometric characteristics of points in the 3-D face model data stored in the first and second data storing module to determine feature points of the 3-D face model data, and assigning different weight ratios to feature points; and a comparing module for comparing the feature points stored in the first and second data storing module, during which, different geometric characteristics being given different weight ratios, and calculating relativity between the feature points to obtain a comparison result.