My gait recognition related papers are here.
With the increasing demands of visual surveillance systems, human identification at a distance is an urgent need. Gait is an attractive biometric feature for human identification at a distance, and recently has gained much interest from computer vision researchers. Gait is a particular way or manner of moving on foot. Compared with those traditional biometric features, such as face, iris, palm print and finger print, gait has many unique advantages such as non-contact, non-invasive and perceivable at a distance. [from scholarpedia.org]
Reducing the Effect of Noise on Human Contour in Gait Recognition
Gait can be easily acquired at a distance, so it has become a popular biometric especially in intelligent visual surveillance. In gait-based human identification there are many factors that may degrade the performance, and noise on human contours is a significant one because to extract contours perfectly is a hard problem especially in a complex background. The contours extracted from video sequences are often polluted by noise. To improve the performance, we have to reduce the effect of noise. Different from the methods which use dynamic time warping (DTW) in previous work to match sequences in the time domain, a DTW-based contour similarity measure in the spatial domain is proposed to reduce the effect of noise. The experiments on a large gait database show the effectiveness of the proposed method.
A Framework for Evaluating the Effect of View Angle, Clothing and Carrying Condition on Gait Recognition
Modelling the Effect of View Variation on Appearance-based Gait Recognition