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A high precision feature based on LBP and Gabor theory for face recognition
Wei Xia1, Shouyi Yin, Peng Ouyang
1Tsinghua Center for Mobile Computing, Institute of Microelectronics, Tsinghua University, Beijing 100084, China. maxiaola@gmail.com
Sensors (Basel, Switzerland)
|April 5, 2013
Summary
A novel feature, BG2D2LRP, enhances face recognition by analyzing texture and contour. This method significantly improves accuracy compared to existing techniques, demonstrating superior performance in experiments.
Area of Science:
- Computer Science
- Biometrics
- Image Processing
Background:
- Accurate image description is crucial for recognition tasks.
- Existing methods face challenges in balancing useful and useless information.
Purpose of the Study:
- To propose a novel, high-precision feature for face recognition.
- To develop a robust face recognition system utilizing this new feature.
Main Methods:
- Introduced BG2D2LRP feature combining static texture and dynamic contour.
- Employed Gabor and Local Binary Patterns (LBP) theory with transformations.
- Utilized block, second derivative, direct orientation, layer, and fusion techniques.
Main Results:
- Achieved a maximum improvement of 29.41% over other methods.
- Experimental validation on seven benchmark face databases (FRGC, AR, FERET).
- Satisfactory performance indicated by Receiver Operating Characteristic (ROC) curves.
Conclusions:
- The proposed BG2D2LRP feature demonstrates feasibility and superiority.
- The new feature and recognition method offer enhanced accuracy and robustness.
- This approach represents a significant advancement in face recognition technology.
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