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Independent component analysis of Gabor features for face recognition
1Dept. of Comput. Sci., New Jersey Inst. of Technol., Newark, NJ, USA.
IEEE Transactions on Neural Networks
|February 2, 2008
Summary
This study introduces Independent Gabor Features (IGFs) for face recognition, achieving high accuracy by extracting independent features using Gabor wavelets and Independent Component Analysis (ICA). The method demonstrates robust performance on diverse datasets.
Area of Science:
- Computer Vision
- Pattern Recognition
- Biometrics
Background:
- Face recognition systems require robust feature extraction methods.
- Traditional methods may struggle with variations in illumination, expression, and pose.
- Gabor features offer spatial locality and selectivity, but redundancy can be an issue.
Purpose of the Study:
- To introduce a novel Independent Gabor Features (IGFs) method for enhanced face recognition.
- To develop a probabilistic reasoning model (PRM) classification method based on IGFs.
- To evaluate the performance of the IGF method on standard face recognition datasets.
Main Methods:
- Deriving Gabor feature vectors from downsampled Gabor wavelet representations of face images.
- Reducing feature vector dimensionality using Principal Component Analysis (PCA).
- Defining independent Gabor features via Independent Component Analysis (ICA) and applying a PRM classifier.
Main Results:
- The IGF method achieved 98.5% accuracy on the FERET dataset with 180 features.
- The IGF method achieved 100% accuracy on the ORL dataset with 88 features.
- The method demonstrated feasibility across variations in illumination, expression, pose, and scale.
Conclusions:
- Independent Gabor Features (IGFs) provide a robust and effective approach for face recognition.
- The integration of Gabor wavelets and ICA significantly enhances feature discrimination.
- The IGF-based PRM classification method shows high accuracy and reliability in real-world scenarios.
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