A general transform, called the geometric transform (GeT), that models the appearance inside a closed contour is proposed. The proposed GeT is a functional of an image intensity function and a region indicator function derived from a closed contour. It can be designed to combine the shape and appearance information at different resolutions and to generate models invariant to deformation, articulation, or occlusion. By choosing appropriate functionals and region indicator functions, the GeT unifies Radon transform, trace transform, and a class of image warpings. By varying the region indicator and the types of features used for appearance modeling, five novel types of GeTs are introduced and applied to fingerprinting the appearance inside a contour. They include the GeTs based on a level set, shape matching, feature curves, and the GeT invariant to occlusion, and a multiresolution GeT (MRGeT). Applications of GeT to pedestrian identity recognition, human body part segmentation, and image synthesis are illustrated. The proposed approach produces promising results when applied to fingerprinting the appearance of a human and body parts despite the presence of nonrigid deformations and articulated motion.

Planar motion is arguably the most dominant type of motion in surveillance videos. The constraints on motion lead to a simplified factorization method for structure from planar motion when using a stationary perspective camera. Compared with methods for general motion, our approach has two major advantages: a measurement matrix that fully exploits the motion constraints is formed such that the new measurement matrix has a rank of at most 3, instead of 4; the measurement matrix needs similar scalings, but the estimation of fundamental matrices or epipoles is not needed. Experimental results show that the algorithm is accurate and fairly robust to noise and inaccurate calibration. As the new measurement matrix is a nonlinear function of the observed variables, a different method is introduced to deal with the directional uncertainty in the observed variables. Differences and the dual relationship between planar motion and planar object are also clarified. Based on our method, a fully automated vehicle reconstruction system has been designed.

We propose a unified framework based on a general definition of geometric transform (GeT) for modeling appearance. GeT represents the appearance by applying designed functionals over certain geometric sets. We show that image warping, Radon transform, trace transform, etc. are special cases of our definition. Moreover, three different types of GeTs are designed to handle deformation, articulation and occlusion and applied to fingerprinting the appearance inside a contour. They include the contour-driven GeT, the feature curve based GeT and selecting functionals to model the appearance inside the convex hull of the contour. A multi-resolution representation that combines both shape and appearance information is also proposed. We apply our approach to image synthesis and object recognition. The proposed approach produces promising results when applied to fingerprinting the appearance of human and body parts despite the challenges due to articulated motion and deformations.

The effects of image and video compression on face recognition in the still-to-video setting are studied in this paper. We use the probabilistic framework described in (S. Zhou et al., Computer Vision and Image Understanding, vol.91, p.214-245, 2003), which solves tracking and recognition problems simultaneously via sequential importance sampling (SIS). To account for the illumination and pose variations in test sequences, intrapersonal space (IPS) is constructed from exemplary views and used to calculate the likelihood density. Both the gallery images and probe videos are compressed and several experiments are run to study their effects on the recognition rate. Some useful conclusions are drawn from the analysis of the experimental results, which are helpful for future research on the interaction between recognition and compression. Meanwhile, the experiments also demonstrate the robustness of the proposed methods.

We report the results of a comparative study on subspace analysis methods for face recognition. In particular, we have studied four different subspace representations and their 'kernelized' versions if available. They include both unsupervised methods such as principal component analysis (PCA) and independent component analysis (ICA), and supervised methods such as Fisher discriminant analysis (FDA) and probabilistic PCA (PPCA) used in a discriminative manner. The 'kernelized' versions of these methods provide subspaces of high-dimensional feature spaces induced by non-linear mappings. To test the effectiveness of these subspace representations, we experiment on two databases with three typical variations of face images, i.e, pose, illumination and facial expression changes. The comparison of these methods applied to different variations in face images offers a comprehensive view of all the subspace methods currently used in face recognition.

We report the results of a comparative study on subspace analysis methods for face recognition. In particular, we have studied four different subspace representations and their 'kernelized' versions if available. They include both unsupervised methods such as principal component analysis (PCA) and independent component analysis (ICA), and supervised methods such as Fisher discriminant analysis (FDA) and probabilistic PCA (PPCA) used in a discriminative manner. The 'kernelized' versions of these methods provide subspaces of high-dimensional feature spaces induced by non-linear mappings. To test the effectiveness of these subspace representations, we experiment on two databases with three typical variations of face images, i.e., pose, illumination and facial expression changes. The comparison of these methods applied to different variations in face images offers a comprehensive view of all the subspace methods currently used in face recognition.