Human gait recognition based on feature extraction of support vector machine and pattern network algorithm

Abstract

Human recognition based on biometric information is important due to its reliability in identity verification. Gait recognition has ability to recognize individuals from a distance. "This study includes human gait recognition based firstly on support vector machine (SVM) and secondly on PatternNet neural network". "Three feature extraction and dimension reduction algorithms were used to increase the recognition performance of these algorithms". These algorithms are: "Liner Discriminant Analysis (LDA), Discrete Fourier Transform (DFT)and Discrete Cosine Transform (DCT)".The performances were compared using mean square error (MSE), PSNR and recognition rate to identify the best model and algorithm. The best results were obtained from the patternNet model especially when it was trained with TrainLM were correct classification rates (CCR) (98%), MSE (0.001) and PSNR (42) where "obtained when adopting LDA algorithm in comparison with DFT and DCT".