Human Gait recognition Using Cross View Micro Gait Dataset with Light weight MobileNet

Gait Analysis in human identification is a pivotal biometric feature which has recently drawn attention in the modern world. Currently, surveillance cameras (in Airports, Banks, etc.) do not always capture the front-view of a human. To resolve the current issue, gait analysis is used to recognize a person. In this study, machine learning and deep learning model are utilized to recognize the human with their gait. Cross View Micro Gait (CVM-GAIT) Dataset is created with numerous individual recorded videos in the cross view with various speeds, which has been converted into frames and stored as images. This study is carried out with SVM, Decision tree, Inception net and the proposed Lightweight Mobile net architecture. The results prove that the proposed model outperforms the state of art with live recorded video.