36
having several different positions due to the slope of the face or eye shape.
3.2 Comparison of RBF SVM classification with
Euclidean The best SVM classification results, using RBF
compared with the results obtained in the validation 2DPCA using euclidean distance. The results
obtained can be seen in Figure 6. In Figure 6, it can be concluded that by using the SVM classification
results obtained are much better than just using euclidean.
3.3 Optimization using GA
After experimenting in the previous stage, the optimization process is carried out using GA on
RBF kernel. Here is an early initialization parameter GA along with the results obtained.
Number of bits per variable = 10 Population size = 5
Lower limit variable C = 1 The upper limit of the variable C = 8
P Cross over= 0.8 P Mutation = 0.1
Lower limit of the variable G = 0:25 The upper limit of the variable G = 2
Figure 7 : Comparison Chart Fitness iterations with the GA
Figure 7 shows that GA has done well optimization. It can be seen from the results
obtained by using the optimal GA scenario is almost the same as the grid search on previous
experiments on CV = 90.12 5 with parameter Gamma = 0.5009 and C = 482.01. In this
experiment GA was not overly contribute significantly to the accuracy of the results obtained.
But the GA parameters obtained in accordance with the tentative conclusion that states that to get the
best accuracy of SVM with RBF kernel , parameter values log
2
G = -1 and C 0. Results of this parameter is applied to the whole training data and
testing to validate the data model has been obtained. The results obtained in the present in
Table 3. Table 3 : Results Testing GA Parameters Optimization in
SVM RBF Kernel
D ata
T P
T N
F N
F P
T raining
1 023
3 200
1 T
esting 1
96 5
94 6
4 6
From the results obtained in testing the data , it appears that accuracy by applying the training
parameters is much lower than the accuracy obtained in the process of the training data . In this
experiment the GA has been successfully performed parameter optimization SVM with RBF
kernel on eye detection system .
4. Conclusion
Detection model eye feature extraction using SVM with 2DPCA produce the highest accuracy on
RBF kernel with Log2 value Gamma = -1 and C 0 with the value of 99.97 on training data and
88.16 on the testing of data.
REFRENCES:
[1] Orman Z, Battal A, Kemer E. A Study on Face, Eye Detection and Gaze Estimation.
International Journal of Computer Science Engineering Survey IJCSES. Volume 2
No.3.
[2] Bhoi N, Mihir MN. Template Matching based Eye Detection in Facial Image.
International
Journal of
Computer Applications 0975
– 8887. Volume 12 No.5. [3] Wang Q, Yang J. Eye Detection in Facial
Images with Unconstrained Background. Journal of Pattern Recognition Research 1.
Volume 1 No 1.
[4] Lessmann S, Stahlbock R, Crone S F. Genetic Algorithms for Support Vector
Machine Model Selection. International Joint Conference on Neural Networks
IJCNN 2006.
[5] Huang C, Wang C. A GA-based feature selection and parameters optimization for
support vector machines. Expert Systems with Applications. Volume 31 Issues 2.
Pages 231
–240. [6] Hoang Le T, Bui L. Face Recognition Based
on SVM and 2DPCA. International Journal of Signal Processing, Image Processing and
Pattern Recognition. Volume 4 No. 3.
[7] Yang J, Zhang D. Two-Dimensional PCA: A New Approach to Appearance-Based
Face Representation and Recognition.
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