Abstract
It is crucial to find methods that analyze large amount of data captured by cameras and/or various sensors installed all around us. Machine learning becomes a prevailing tool in analyzing such data that signifies behavioral characteristics of human beings. Gait as an identifier for use in individual recognition systems has respective and almost certainly unique key features for each person including centroid, cycle length and step size. Gait is sometimes preeminent suited to recognition or surveillance scenarios. It might be used in the identification of females who are wearing veils in some countries without critical social issues. The objective of this project is to predict accurately one-dimensional coordinates of normalized n-component vectors representing two-dimensional silhouettes in order to identify individuals at a distance without any interaction and obtrusion. Varied algorithms are further incorporated into walk pattern analysis to adoptively improve gait recognitions and classification. The results are reported reasonable identification performance as compared to several machine learning methods.
Highlights
Biometrics as automated techniques are constantly used to approve identities of human beings
Human motion analysis are necessary in various areas of computer science such as biometrics, computer graphics and games industry
Human motion has a number of advantages in gender classification
Summary
Biometrics as automated techniques are constantly used to approve identities of human beings. Most of the current biometric systems are essentially pattern recognition systems, for references see (Sayed and Jradi, 2014), (Vacca, 2007), (Raina, 2011), (Raina and Pandey, 2011) and (Jain and Aggarwal, 2012). These systems identify a person by calibrating the authenticity of specific physiological or behavioral characteristics possessed by that person. The aim of gait recognition is to discriminate an individual by analyzing his/her shape which changes over time in an image sequence. In (Boyd and Little, 2005) an early study of the existing gait and quasi-gait recognition systems are categorized by their source of oscillations: Shape, joint trajectory, self-similarity and pixel
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