This paper presents a new approach to gait identification and authentication with simple representation and lower computational complexity

which can meet intelligent surveillance’s need in precision and response. It creates Gaussian Mixture Model for each scenario

and contour of gait is extracted from binary silhouette for Euclidean distance between the centroid and any pixel on it. Contour is unfolded clockwise by the distance from the uppermost pixel

and then 2D features are transformed into 1D and normalized according to a standard model of gait. Thresholds are determined by dynamic time warping (DTW) distance between training sequences and standard model. Finally

gait recognition is performed by comparing DTW distance of testing sequences with predetermined threshold. Compared with other methods

it balances both computational cost and recognition rate

and achieves performance of intelligent surveillance.