The Research Of Statistical Moments Gait Recognition Technology Based On Radon Transform

As a rising biometric recognition technology, the purpose of gait recognition is to authenticate the identity of people by their posture of walking. It is one of the most potential biometric features. So it has been applied in intelligent supervision, interface between human and computers, and analyzing the behavior of human.Two statistical moments gait recognition methods based on the Radon transform are developed in this thesis. The proposed algorithms are to firstly restore the background of the gait image sequences by adaptive background detecting algorithm. Then images are analyzed to attain binary region of the person. Based on the region width, key frames are separated from every gait cycles. The first method using Radon invariant moments extract the features of key frames by expressing the invariance properties of the images including translation, scale and rotation change. And the second method using Radon velocity moments proposed as a new statistical moment features express the spatial and temporal correlation of projection images. Finally, the two kinds of feature vectors are trained and detected by SVM.The feasibilities of the two algorithms are supported by applying the algorithms on two different gait databases. A greater recognition ratio is achieved, which proves that they can be widely applied in the gait recognition domain in the future.