Performance Analysis of Different Feature Extraction Algorithms Used with Particle Swarm Optimization for Gait Recognition System
Omaima N. Ahmad AL-Allaf1, Shahlla A. AbdAlKader2
1Dr. Omaima N. Ahmad AL-Allaf, Department of Basic Sciences, Faculty of Sciences and Information Technology, AL-Zaytoonah University of Jordan, P.O. Box 130, Amman (11733), Jordan. Lecturer.
2Shahlla A.AbdAlKader, Department of Computer Systems, Foundation of Technical Education, Technical Institute, Mosul, Iraq.
Manuscript received on 23 May 2015 | Revised Manuscript received on 30 May 2015 | Manuscript published on 30 May 2015 | PP: 23-30 | Volume-4 Issue-2, May 2015 | Retrieval Number: B1405054215©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Recently, person identification systems based on gait recognition had been gained growing large interest from researchers in the fields of artificial intelligence and image processing Thus, a gait recognition system based on particle swarm optimization (PSO) has been suggested in this work to recognize any person at a distance who performing the movement. Three feature extraction and dimension reduction algorithms were used to increase the recognition performance of PSO algorithm. These algorithms are: Liner Discriminant Analysis (LDA); Discrete Fourier Transform (DFT); and Discrete Cosine Transform (DCT). Many experiments were conducted for PSO with the three algorithms using different: swarm size, block dimension and number of iterations. Best results obtained when selecting swarm size equal 40, feature block size 70×70 and 100 number of iterations. At the same time best results of: recognition rate (97%), MSE (0.0027) and PSNR (38) where obtained when adopting LDA algorithm in comparison with DFT and DCT. And also the results obtained from DFT are better than the results obtained from using DCT. The time required for executing the LDA is lowest than the time required for executing DFT and DCT. DCT require more time than the other used feature extraction algorithms.
Keyword: Gait Recognition, Practical Swarm Optimization (PSO), Liner Discriminant Analysis (LDA), Discrete Cosine Transform (DCT), Discrete Fourier Transform (DFT)
Scope of the Article: Predictive Analysis