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...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-01-01
|
| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/835607 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832547507741130752 |
|---|---|
| author | Mohammed Hasan Abdulameer Siti Norul Huda Sheikh Abdullah Zulaiha Ali Othman |
| author_facet | Mohammed Hasan Abdulameer Siti Norul Huda Sheikh Abdullah Zulaiha Ali Othman |
| author_sort | Mohammed Hasan Abdulameer |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-55207d77b8c04ad888b03c7db04f7621 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-55207d77b8c04ad888b03c7db04f76212025-02-03T06:44:34ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/835607835607Support Vector Machine Based on Adaptive Acceleration Particle Swarm OptimizationMohammed Hasan Abdulameer0Siti Norul Huda Sheikh Abdullah1Zulaiha Ali Othman2Pattern Recognition Research Group, Centre for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bandar Baru Bangi, MalaysiaPattern Recognition Research Group, Centre for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bandar Baru Bangi, MalaysiaData Mining and Optimization Group, Centre for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bandar Baru Bangi, MalaysiaExisting 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.http://dx.doi.org/10.1155/2014/835607 |
| spellingShingle | Mohammed Hasan Abdulameer Siti Norul Huda Sheikh Abdullah Zulaiha Ali Othman Support Vector Machine Based on Adaptive Acceleration Particle Swarm Optimization The Scientific World Journal |
| title | Support Vector Machine Based on Adaptive Acceleration Particle Swarm Optimization |
| title_full | Support Vector Machine Based on Adaptive Acceleration Particle Swarm Optimization |
| title_fullStr | Support Vector Machine Based on Adaptive Acceleration Particle Swarm Optimization |
| title_full_unstemmed | Support Vector Machine Based on Adaptive Acceleration Particle Swarm Optimization |
| title_short | Support Vector Machine Based on Adaptive Acceleration Particle Swarm Optimization |
| title_sort | support vector machine based on adaptive acceleration particle swarm optimization |
| url | http://dx.doi.org/10.1155/2014/835607 |
| work_keys_str_mv | AT mohammedhasanabdulameer supportvectormachinebasedonadaptiveaccelerationparticleswarmoptimization AT sitinorulhudasheikhabdullah supportvectormachinebasedonadaptiveaccelerationparticleswarmoptimization AT zulaihaaliothman supportvectormachinebasedonadaptiveaccelerationparticleswarmoptimization |