Support Vector Machine Based on Adaptive Acceleration Particle Swarm Optimization

Existing face recognition methods utilize particle swarm optimizer (PSO) and opposition based particle swarm optimizer (OPSO) to optimize the parameters of SVM. However, the utilization of random values in the velocity calculation decreases the performance of these techniques; that is, during the ve...

Full description

Saved in:
Bibliographic Details
Published in:TheScientificWorld 2014-01, Vol.2014 (2014), p.1-8
Main Authors: Abdulameer, Mohammed Hasan, Othman, Zulaiha Ali, Sheikh Abdullah, Siti Norul Huda
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Bioinformatics
Biometrics
Classification
Face
Heuristic
Information science
Machine learning
Mathematical optimization
Mathematical research
Methods
Optimization
Pattern recognition
Pattern Recognition, Automated
Population
Science
Support Vector Machine
Training
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Existing face recognition methods utilize particle swarm optimizer (PSO) and opposition based particle swarm optimizer (OPSO) to optimize the parameters of SVM. However, the utilization of random values in the velocity calculation decreases the performance of these techniques; that is, during the velocity computation, we normally use random values for the acceleration coefficients and this creates randomness in the solution. To address this problem, an adaptive acceleration particle swarm optimization (AAPSO) technique is proposed. To evaluate our proposed method, we employ both face and iris recognition based on AAPSO with SVM (AAPSO-SVM). In the face and iris recognition systems, performance is evaluated using two human face databases, YALE and CASIA, and the UBiris dataset. In this method, we initially perform feature extraction and then recognition on the extracted features. In the recognition process, the extracted features are used for SVM training and testing. During the training and testing, the SVM parameters are optimized with the AAPSO technique, and in AAPSO, the acceleration coefficients are computed using the particle fitness values. The parameters in SVM, which are optimized by AAPSO, perform efficiently for both face and iris recognition. A comparative analysis between our proposed AAPSO-SVM and the PSO-SVM technique is presented.
ISSN:2356-6140
1537-744X
1537-744X
DOI:10.1155/2014/835607