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- W4367011819 abstract "In this paper, an attempt has been made to make a low-cost, marker-less, distance independent biometric identification system that will be able to analyze gait parameters from the walking video sequence of a person at a variable distance from the camera, train itself and later will be able to identify the same person by observing his/her gait pattern with the knowledge it has been trained with. Gait parameters analyzed in this system are heel strike angle, toe-off angle and stride angle. The system is based on three models: support vector machine (SVM), K-nearest neighbors (KNN) and random forest. A comparison of the result of these three classification models has also been made. Also, the effect of individual parameters upon the classification has been shown." @default.
- W4367011819 created "2023-04-27" @default.
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- W4367011819 date "2023-01-01" @default.
- W4367011819 modified "2023-09-26" @default.
- W4367011819 title "Identification of Humans by Using Machine Learning Models on Gait Features" @default.
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- W4367011819 doi "https://doi.org/10.1007/978-981-19-5191-6_12" @default.
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