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Spatially optimized data-level fusion of texture and shape for face recognition
Faisal R Al-Osaimi1, Mohammed Bennamoun, Ajmal Mian
1Department of Computer Engineering, Umm Al-Qura University, Makkah, Saudi Arabia. frosaimi@uqu.edu.au
Summary
We introduce a novel data-level fusion technique for 3-D face recognition, optimizing spatial and nonlinear models. This method enhances invariance to expression and illumination, outperforming score-level fusion.
Area of Science:
- Computer Vision
- Biometrics
- Pattern Recognition
Background:
- Data-level fusion shows promise for enhancing human face recognition.
- Current techniques face challenges in fully realizing this potential due to expression and illumination variations.
Purpose of the Study:
- To propose a spatially optimized data/pixel-level fusion of 3-D shape and texture for invariant face recognition.
- To enhance face recognition capabilities by modeling expression and illumination variations.
Main Methods:
- Spatially optimized data/pixel-level fusion using 3-D shape and texture.
- Objective optimization of fusion functions within linear subspaces for invariance.
- Constraining adjacent function parameters for smooth variation and numerical regularization.
- Combining multiple nonlinear fusion models to improve learning capabilities.
Main Results:
- Spatial optimization, higher-order fusion functions, and combined models systematically improve performance.
- Achieved performance surpassing score-level fusion in a comparable experimental setup for the first time.
- Demonstrated effectiveness on the FRGC v2 dataset.
Conclusions:
- The proposed spatially optimized fusion method significantly enhances 3-D face recognition.
- The approach effectively models and mitigates variations from expression and illumination.
- This technique represents a breakthrough, outperforming existing fusion strategies.
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