Gait Biometric Recognition Using Direct Classification, TSVM, SVM and Neural Network

Abstract

Gait recognition is the process of identifying an individual by the manner in which they walk. Using gait as a biometric is a relatively new area of study, within the realms of computer vision. It has been receiving growing interest within the computer vision community and a number of gait metrics have been developed. The term gait recognition to signify the identification of an individual from a video sequence of the subject walking. This does not mean that gait is limited to walking, it can also be applied to running or any means of movement on foot. While gait has several attractive properties as a biometric there are several confounding factors such as variations due to footwear, terrain, fatigue, injury, and passage of time. Examples of motion that are gaits include walking, running, jogging, and climbing stairs. Sitting down, picking up an object, and throwing and object are all coordinated motions, but they are not cyclic. Jumping jacks are coordinated and cyclic, but do not result in locomotion. The use of gait as a biometric for human identification is still young when compared to methods that use voice, finger prints, or faces.