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- W2332005488 abstract "Gait recognition addresses the problem of human identification at a distance by identifying people based on the way they walk. Therefore, gait recognition has gained growing interest from researchers in recent years. This work presents gait recognition system based on particle swarm optimization (PSO) to recognize a person performing the movement for person identification. The system is based on Discrete Cosine Transform (DCT) for reducing dimensionality and feature extraction. Many experiments were conducted using different: swarm size, block dimension and number of iterations. The results showed that increasing the swarm size to 40 particles and also increasing block size of DCT sub image to (70×70) pixels will increase the overall performance of gait recognition system. The recognition rate reached 96%, MSE reached 0.0088 and PSNR reached 35%. Keywords— Gait Recognition, Person Identification, Practical Swarm Optimization (PSO), Discrete Cosine Transform (DCT)" @default.
- W2332005488 created "2016-06-24" @default.
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- W2332005488 date "2015-05-12" @default.
- W2332005488 modified "2023-09-24" @default.
- W2332005488 title "Particle Swarm Optimization Based Discrete Cosine Transform for Person Identification by Gait Recognition" @default.
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- W2332005488 doi "https://doi.org/10.15849/icit.2015.0023" @default.
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