@article{MAKHILLJEAS201914917770,
    title = {Face Recognition Approach using an Enhanced Particle Swarm Optimization and
Support Vector Machine},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {14},
    number = {9},
    pages = {2982-2987},
    year = {2019},
    issn = {1816-949x},
    doi = {jeasci.2019.2982.2987},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2019.2982.2987},
    author = {Wasan Kadhim,Waheb and},
    keywords = {Support Vector Machine (SVM),Particle Swarm Optimization (PSO),Opposition PSO (OPSP),Adaptive Acceleration PSO (AAPSO),face recognition,PCA},
    abstract = {Face recognition is one of the most promising research area in the last decades. The SVM approach
is one of the famous approaches in machine learning fields because it can determine the global optimum
solutions with lesser number of training samples especially, complex non-linear challenges such as in face
recognition applications. Though, there is an important issue that can affects the whole classification process
which is picking the optimum parameters of SVM. Recently, Particle Swarm Optimization (PSO) is used to
discover the optimal parameters of SVM and many versions of PSO are used for this purpose, like: PSO-SVM
technique, opposition PSO and SVM which called (OPSO-SVM) technique and AAPSO-SVM technique which
represents adaptive acceleration PSO and SVM. In this study, a new hybrid technique based on the
combination of "Accelerated PSO" and "OPSO-SVM" is introduced for face recognition applications. The
hybridization can improve the convergence speed in PSO in order to find the optimal parameters of SVM. In the
feature extraction process, the PCA algorithm is used for that purpose and the resulted features are delivered
to the proposed technique in order to classify the face images. Two human face datasets are used in the
experimentation stage such as, SCface dataset and CASIA face dataset in order to validate the performance of
the proposed technique. The comparison process for proposed technique with the other recent technique, like:
PSO-SVM, OPSO-SVM and AAPSO-SVM is done as an assessment process. The proposed technique provided
high accuracy for recognition when we compared it with the other techniques and it was robust in finding the
optimal parameters of SVM.}
    }