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Human gait recognition is a 2nd Generation Biometrics which focuses on distance and is unobtrusive. Human gait recognition is identification of a person based on their walking style. It is a Human Cooperation free biometric system. Many studies related to gait include subjects walking well below or above comfortable (free) speed. For this reason, a descriptive study examining the effect of walking speed on gait was conducted. The purpose of the study was to create a single-source, readily accessible repository of comprehensive gait data for a large group of children walking at a wide variety of speeds. Regression analysis is a set of statistical processes for estimating the relationships between a dependent variable (often called the 'outcome variable') and one or more independent variables (often called 'predictors', 'covariates', or 'features'). A Complete gait cycle is obtained when all the values are present from 0 to 100% completely. If there are any missing values in the gait cycle it is referred to as incomplete gait cycle. The prediction of the person speed or recognizing the person based on incomplete values becomes a complex task .So in order to predict or estimate the missing speed value we use the Polynomial regression technique. To predict the missing value we created Polynomial regression models. Thus, we try to compare these models with the Linear Regression models. Keywords: Gait, Biometrics, Regression Analysis, Gait Cycle, Polynomial Regression |
