TY  - JOUR
T1  - Parameter Optimization of Support Vector Machine Using Enhanced Hybrid
Particle Swarm Optimization in Non-Linear Face Authintication Problem
AU - Ali Hussein, Hasanain AU - Abdul Wahab Habeeb, Haidar 
JO  - Journal of Engineering and Applied Sciences
VL  - 13
IS  - 15
SP  - 6162
EP  - 6166
PY  - 2018
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2018.6162.6166
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2018.6162.6166
KW  - optimization
KW  -PSO
KW  -face recognition
KW  -Support vector machine
KW  -LOPSO-SVM
KW  -AOPSO-SVM
AB  - One of the well-known machine learning methods is Support Vector Machine (SVM). With small
number of training samples, it can discover the global optimal solutions for the complex non-linear problems
such as face recognition. However, choosing the optimal parameters of SVM is a big challenge which has a high
impact in the classification results. "Particle Swarm Optimization (PSO)" has been used to find the optimal
parameters of SVM but PSO has drawbacks in inertia weight selection which is fixed number and in population
initialization which is random. In this study, a new face recognition technique based on Hybrid Particle Swarm
Optimization and Support Vector Machine (LOPSO-SVM) is introduced. The hybrid PSO algorithm based on
Logarithm decreasing inertia weight and opposition particle swarm initialization which can improve the
convergence speed in PSO. Principle Component Analysis (PCA) has been used for feature extraction process
and the extracted features was passed to the proposed technique. In the experimental results, human face
database CASIA V5 is utilized to verify the performance of face recognition technique LOPSO-SVM. The
proposed technique is compared with PSO-SVM and AOPSO-SVM. The experimental results shows that the
proposed method gave higher face recognition accuracy than PSO-SVM and AOPSO-SVM and outperform the
other method in finding the optimal parameters of SVM.
ER  -