Analysis of Gait Recognition Algorithm Models using SURF and SVM – A Comparison

Abstract: Now-a-days, biometric recognition is a common and reliable way to authenticate any human being based on his physiological or behavioural biometrics [1]. A physiological biometric traits is stable in their biometric like fingerprint, iris pattern, facial feature, hand geometry, gait pattern etc. whereas behavioral biometric traits is related to the behaviour of person such as signature, speech pattern, keystroke pattern. Facial recognition system is a computer application for automatically identifying or verifying a person from a digital image or a video frame from a video source. A specific way or style of proceeding onward foot is characterized as Gait. Gait acknowledgment could be used from a detachment that makes it proper to perceive the offenders doing incorrectly work. In this paper we utilize Hananava's model which is a geometric human body model and an aggregate of 41 Anthropometric parameters should have been measured. The systems being utilized for acknowledgment are SURF and SVM. SURF is a vigorous neighbourhood highlight finder though SVM is a cutting edge characterization strategy. The yield result is being acquired from the blend of these two calculations. Yield is through coordinating the video information outlines with recordings yield outlines.

Keywords: Gait Recognition, Speeded up Robust Features (SURF), Support Vector Machine (SVM).