Human gait recognition based on Discrete Cosine Transform and Linear Discriminant Analysis

Fan, Zheyi; Jiang, Jiaojiao; Weng, Shuqin; He, Zhonghang; Liu, Zhiwen

doi:10.1109/icspcc.2016.7753698

Cited by 3 publications

(1 citation statement)

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“…It is also used in detecting diseases related to gait such as Parkinson's (Xu et al, 2018). Discrete cosine transform (DCT) had been introduced by Nasir et al, 1974, and it is used widely in image processing, therefore many researcher used in recognition process to extract features Shahlla and Omaima, 2015;and Zheyi et al, 2016. A number of studies have used PSO for implementing gait analysis to minimize recognition time. Most of them have noise ratio problem and large computational time, whereas few studies used parallel computing (PC) to reduce computation time.…”

Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimization on Parallel Computers for Improving the Performance of a Gait Recognition System

Abdulqader

Krekorian

2019

Polytechnic j.

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In recent years, the gait recognition (GR) using particle swarm optimization (PSO) algorithm (OSO) has been execute very fast and accurate with single computer, but with the appearance of parallel computing (PC), it was necessary to use this technique to improve the results of GR. This study presents the use of parallel computing approaches (PCA) to implement PSO for a GR system (GRS) to decrease processing while maintaining reconstructed image quality. These approaches are: Codistributor and parallel cluster. Many experiments have been executed with recognition between the two approaches. The experimental results showed that increasing the PSO swarm size, decreasing number of iterations, and increasing number of workers used for the PCA can reduce recognition time and increase performance. Best results were obtained from implementing parallel computing with eight workers and 100 iterations. The execution time reached 4s and PSNR reached 44db. At the same time, the best results were obtained from PCL approach.

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Section: Introductionmentioning

confidence: 99%