Survey of Current Trends in Human Gait Recognition Approaches.

Abstract

Introduction:-Gait is one of the few biometric features that can be measured remotely without physical contact and proximal sensing, which makes it useful in surveillance applications. Such applications play a decisive role in monitoring high security areas including banks, airports, military bases and railway stations. In the real world, there are various factors, significantly affecting human gait including clothes, shoes, carrying objects, walking surfaces, walking speeds and observed views. A large number of gait recognition methods have been published recently, which can be roughly divided into two categories, model-based methods include "A new view-invariant feature for cross-view gait recognition" and appearance-based method include "Recognizing gaits across views through correlated motion co-clustering". These methods require a preprocessing of foreground/background segmentation (FG/BG) on a gait video, in order to extract shape contours, silhouettes, skeletons, or body joints for further gait analysis. The modelbased methods generally aim to model kinematics of human joints in order to measure physical gait parameters such as trajectories, limb lengths and angular speeds. The appearance-based methods typically analyze gait sequences without explicit modeling of human body structure. These methods have shown their effectiveness on human gait recognition under fixed view. However, they lack a proper methodology to address the problem of view change.