TY  - JOUR
T1  - A New Technique for Support Vector Machine Parameters Optimization
Based on Modiefied PSO Algorithm
AU - Abbas, Sabah Khudhair AU - Laftah, Abdullah Aziz AU - Rjeib, Hasanein D. 
JO  - Journal of Engineering and Applied Sciences
VL  - 14
IS  - 13
SP  - 4597
EP  - 4602
PY  - 2019
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2019.4597.4602
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2019.4597.4602
KW  - Biometric
KW  -optimization
KW  -face recognition
KW  -PSO
KW  -SCface
KW  -experiments
AB  - Support vector machine can determine the global finest solutions in many complicated problems and
it is widely used for human face classification in the last years. Nevertheless, one of the main limitations of SVM
is optimizing the training parameters, especially when SVM used in face recognition domains. Various
methodologies are used to deal with this issue such as PSO, OPSO, AAPSO and AOPSO. Nevertheless, there
is a room of advancements in this kind of optimization process. Lately, an improved version of PSO is
developed which is called modified PSO. In this study, a new technique based on modified PSO, called
(Modified PSO-SVM) is proposed to optimize SVM parameters. The proposed scheme utilizes modified PSO
to seek the finest parameters of SVM two human face datasets: SCface, CASIAV5 and CMU Multi-PIE face
datasets are used in the experiments. Then, a comparison is done with the PSO-SVM, OPSO-SVM and
AOPSO-SVM and it showed promising results in terms of accuracy.
ER  -