Omaima N. Ahmad AL-Allaf, and Shahlla A. AbdAlKader
International Journal of Recent Technology and Engineering (IJRTE)
ISSN: 2277-3878, Volume-4, Issue-2, May 2015
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.