%A sequence of deforming shapes has been used as an important cue for deformable object recognition and classification.
We present a framework to compare two sequences of deforming shapes using both parametric models and non-parametric methods.
In our approach, Kendall's definition of shape is used as the shape feature.
Since the shape feature lives on a non-Euclidean manifold, we propose parametric models like the autoregressive model and the autoregressive moving average model on the tangent space and demonstrate the ability of these models to capture the nature of shape deformations by performing experiments on gait recognition.
%We also provide results for synthesis of deforming shapes using the parametric model learnt.
The non-parametric model is based on Dynamic Time-Warping.
We suggest a modification of the Dynamic time-warping algorithm to include the nature of the non-Euclidean space in which the shape deformations take place.
We also show the efficacy of this algorithm by its application to gait recognition.
We consider the shape deformations of a person's silhouette as a discriminating feature and provide recognition results using the non-parametric model.
Our analysis leads to some interesting observations on the role of shape and kinematics in automated gait recognition.


