Biometric Authorization System Using Gait Biometry
Human gait, which is a new biometric aimed to recognize individuals by the way they walk have come to play an increasingly important role in visual surveillance applications. In this paper a novel hybrid holistic approach is proposed to show how behavioral walking characteristics can be used to recognize unauthorized and suspicious persons when they enter a surveillance area. Initially background is modeled from the input video captured from cameras deployed for security and the foreground moving object in the individual frames are segmented using the background subtraction algorithm. Then gait representing spatial, temporal and wavelet components are extracted and fused for training and testing multi class Support Vector Machine models (SVM). The proposed system is evaluated using side view videos of NLPR database.