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.

Ashok Veeraraghavan, Amit Roy Chowdhury and Rama Chellappa. Matching Shape Sequences in Video with an application to Human Movement Analysis. Accepted For Publication in IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI). [pdf] [ppt]

This paper presents a method to recover the 3D configuration of a face in each frame of a video. The 3D configuration consists of the 3 translational parameters and the 3 orientation parameters which correspond to the yaw, pitch and roll of the face, which is important for applications like face modeling, recognition, expression analysis, etc. The approach combines the structural advantages of geometric modeling with the statistical advantages of a particle-lter based inference. The face is modeled as the curved surface of a cylinder which is free to translate and rotate arbitrarily. The geometric modeling takes care of pose and self-occlusion while the statistical modeling handles moderate occlusion and illumination variations. Experimental results on multiple datasets are provided to show the efficacy of the approach. The insensitivity of our approach to calibration parameters (focal length) is also shown.

Gaurav Aggarwal, Ashok Veeraraghavan and Rama Chellappa. "3D Facial Pose Tracking in Uncalibrated Videos". International Conference on Pattern Recognition and Machine Intelligence(PReMI), 2005. Published in Lecture Notes in Computer Science, Volume 3776, Dec 2005, Pages 515-520 [pdf] [ppt] [TrackingResult1] [TrackingResult2]

Human gait and activity analysis from video is presently attracting a lot of attention in the computer vision community. In this paper, we analyze the role of two of the most important cues in human motion- shape and kinematics. We present an experimental framework whereby it is possible to evaluate the relative importance of these two cues in computer vision based recognition algorithms. In the process, we propose a new gait recognition algorithm by computing the distance between two sequences of shapes that lie on a spherical manifold. In our experiments, shape is represented using Kendall's definition of shape. Kinematics is represented using a Linear Dynamical system. We place particular emphasis on human gait. Our conclusions show that shape plays a role which is more significant than kinematics in human identification using gait. As a natural extension we study the role of shape and kinematics in activity recognition. Our experiments indicate that we require models that contain both shape and kinematics in order to perform accurate activity classification. These conclusions also allow us to explain the relative performance of many existing methods in computer-based human activity modeling.

Ashok Veeraraghavan, Amit Roy Chowdhury and Rama Chellappa. Role of Shape and Kinematics in Human Movement Analysis, in IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), June 2004. [pdf] [ppt]