A novel method is introduced to recognize and estimate the scale of time-varying human gestures. It exploits the changes in contours along spatio-temporal directions. Each contour is first parameterized as a 2D function of radius vs. cumulative contour length, and a 3D surface is composed from a sequence of such functions. In a two-phase recognition process, Dynamic TimeWarping is employed to rule out significantly different gesture models, and then Mutual Information (MI) is applied for matching the remaining models.

The system has been tested on 8 gestures performed by 5 subjects with varied time scales. The two-phase process is compared against exhaustively testing three similarity measures based upon MI, Correlation, and Nonparametric Kernel Density Estimation. Experimental results demonstrate that the exhaustive application of MI is the most robust with a recognition rate of 90.6%, however, the two-phase approach is much more computationally efficient with a comparable recognition rate of 90.0%.