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Human gait recognition using depth camera: a covariance based approach

Gait is an important biometric modality for recognizing humans. Unlike other biometrics, human gait can be captured at a distance which makes it an unobtrusive method for recognition. In this paper, an unrestricted gait recognition algorithm is proposed which uses 3D skeleton information and trajectory covariance of joint points. 3-D skeleton is generated from the depth images that are captured using Kinect sensor. The temporal tracking of skeleton points is used for gait analysis. The covariance measure between these skeleton point trajectories are computed and the covariance matrices form the gait model. The gait is recognized by computing the minimum dissimilarity measure between the gait models of the training data and the testing data. Recognition accuracy of over 90% has been achieved for a data set consisting of fixed and moving camera scenarios of 20 subjects.