Face Recognition with Symmetrical Face Training Samples Based on Local Binary Patterns and the Gabor Filter
Abstract
:1. Introduction
2. Literature Review
3. Preprocessing Methods
3.1. Wavelet Transforms
3.2. Gaussian Low-Pass Filter (GLPF)
3.3. Difference of Gaussians (DoG)
4. Feature Extraction Methods
4.1. Feature Extraction Using GLCM
4.2. Feature Extraction Using LBP
4.3. Feature Extraction Using the Gabor Filter
5. Classification
5.1. Euclidean Distance
6. Dataset
6.1. The ORL Dataset
6.2. The Yale Dataset
7. Experiments and Results
7.1. Generating New Images
7.2. Experiments on the ORL Dataset
7.3. Experiments on Symmetrical ORL Dataset
7.4. Using a Preprocessing Stage
7.5. The GLCM Method
7.6. Combining Feature Extraction Methods
7.7. The Gabor Filter Method
7.8. Other Experiments
7.9. Experiments on the Yale Dataset
8. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Preprocessing Method | Feature Extraction Method | No. of Training Images | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||
Recognition Rate % | |||||||||||
No | LBP | OTS | 62.5 | 75 | 82.5 | 82.5 | 90 | 90 | 92.5 | 92.5 | 92.5 |
OSTS | 67.5 | 77.5 | 87.5 | 87.5 | 92.5 | 95 | 95 | 95 | 95 | ||
DWT | LBP | OTS | 70 | 80 | 90 | 92.5 | 95 | 95 | 95 | 97.5 | 97.5 |
OSTS | 72.5 | 82.5 | 92.5 | 95 | 97.5 | 97.5 | 97.5 | 97.5 | 97.5 | ||
GLPF | LBP | OTS | 72.5 | 80 | 87.5 | 92.5 | 95 | 95 | 97.5 | 97.5 | 97.5 |
OSTS | 75 | 87.5 | 90 | 97.5 | 97.5 | 97.5 | 97.5 | 100 | 100 | ||
DoG | LBP | OTS | 50 | 55 | 62.5 | 62.5 | 65 | 65 | 65 | 67.5 | 70 |
OSTS | 52.5 | 60 | 62.5 | 67.5 | 67.5 | 67.5 | 67.5 | 72.5 | 72.5 | ||
No | GLCM | OTS | 55 | 65 | 77.5 | 77.5 | 80 | 87.5 | 87.5 | 87.5 | 90 |
OSTS | 60 | 67.5 | 85 | 85 | 87.5 | 90 | 90 | 90 | 90 | ||
DWT | GLCM | OTS | 50 | 55 | 60 | 60 | 62.5 | 62.5 | 62.5 | 65 | 62.5 |
OSTS | 57.5 | 70 | 70 | 80 | 77.5 | 75 | 80 | 80 | 82.5 | ||
DoG | GLCM | OTS | 37.5 | 40 | 40 | 40 | 42.5 | 42.5 | 45 | 45 | 45 |
OSTS | 40 | 42.5 | 50 | 50 | 50 | 50 | 50 | 52.5 | 55 | ||
GLPF | GLCM | OTS | 57.5 | 67.5 | 80 | 80 | 80 | 82.5 | 82.5 | 82.5 | 82.5 |
OSTS | 57.5 | 75 | 82.5 | 82.5 | 87.5 | 90 | 90 | 90 | 90 | ||
No | LBP–GLCM | OTS | 70 | 82.5 | 87.5 | 92.5 | 92.5 | 95 | 95 | 95 | 95 |
OSTS | 72.5 | 85 | 90 | 95 | 95 | 97.5 | 97.5 | 100 | 100 | ||
No | Gabor | OTS | 87.5 | 95 | 100 | 100 | 100 | 100 | 100 | 100 | 100 |
OSTS | 90 | 97.5 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | ||
No | PCA | OTS | 69 | 79 | 84 | 87 | 89 | 95 | 96 | 96 | 95 |
No | CRC | OTS | 72 | 84 | 86 | 91 | 91 | 94 | 93 | 94 | 93 |
No | SRC | OTS | 76 | 89 | 90 | 94 | 94 | 94 | 95 | 96 | 95 |
No | SCRC | OSTS | 76 | 90 | 92 | 94 | 94 | 95 | 96 | 96 | 95 |
Preprocessing Method | Feature Extraction Method | No. of Training Images | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | |||
Recognition Rate % | ||||||||||||
No | LBP | OTS | 90 | 93 | 96 | 98 | 100 | 100 | 100 | 100 | 100 | 100 |
OSTS | 95 | 98 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | ||
No | GLCM | OTS | 70 | 75 | 87 | 87 | 87 | 87 | 87 | 87 | 87 | 87 |
OSTS | 75 | 80 | 93 | 93 | 93 | 93 | 93 | 93 | 93 | 93 | ||
DWT | GLCM | OTS | 12 | 15 | 18 | 20 | 20 | 20 | 20 | 20 | 20 | 20 |
OSTS | 20 | 23 | 27 | 30 | 33 | 33 | 33 | 33 | 33 | 33 | ||
DoG | GLCM | OTS | 60 | 60 | 73 | 73 | 73 | 73 | 80 | 80 | 80 | 80 |
OSTS | 65 | 67 | 87 | 87 | 87 | 87 | 87 | 87 | 87 | 87 | ||
GLPF | GLCM | OTS | 80 | 85 | 87 | 87 | 87 | 87 | 87 | 87 | 87 | 87 |
OSTS | 80 | 93 | 93 | 93 | 93 | 93 | 93 | 93 | 93 | 93 | ||
No | Gabor | OTS | 95 | 97 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 |
OSTS | 97 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | ||
No | PCA | OTS | 69 | 89 | 89 | 93 | 87 | 87 | 98 | 95 | 96 | 100 |
No | CRC | OTS | 87 | 93 | 94 | 99 | 98 | 96 | 98 | 95 | 96 | 100 |
No | SRC | OTS | 87 | 90 | 90 | 98 | 92 | 92 | 98 | 100 | 100 | 100 |
No | SCRC | OSTS | 88 | 95 | 97 | 99 | 100 | 100 | 100 | 100 | 100 | 100 |
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Allagwail, S.; Gedik, O.S.; Rahebi, J. Face Recognition with Symmetrical Face Training Samples Based on Local Binary Patterns and the Gabor Filter. Symmetry 2019, 11, 157. https://doi.org/10.3390/sym11020157
Allagwail S, Gedik OS, Rahebi J. Face Recognition with Symmetrical Face Training Samples Based on Local Binary Patterns and the Gabor Filter. Symmetry. 2019; 11(2):157. https://doi.org/10.3390/sym11020157
Chicago/Turabian StyleAllagwail, Saad, Osman Serdar Gedik, and Javad Rahebi. 2019. "Face Recognition with Symmetrical Face Training Samples Based on Local Binary Patterns and the Gabor Filter" Symmetry 11, no. 2: 157. https://doi.org/10.3390/sym11020157