Learn More
We extend the work of Black and Yacoob on the tracking and recognition of human facial expressions using parameterized models of optical flow to deal with the articulated motion of human limbs. We define a " cardboard person model " in which a person's limbs are represented by a set of connected planar patches. The parameterized image motion of these(More)
This paper explores the use of local parametrized models of image motion for recovering and recognizing the non-rigid and articulated motion of human faces. Parametric flow models (for example affine) are popular for estimating motion in rigid scenes. We observe that within local regions in space and time, such models not only accurately model non-rigid(More)
A framework for modeling and recognition of temporal activities is proposed. The modeling of sets of exemplar activities is achieved by parameteriz-ing their representation in the form of principal components. Recognition of spatio-temporal variants of modeled activities is achieved by parame-terizing the search in the space of admissible transformations(More)
This paper explores the use of local parametrized models of image motion for recovering and recognizing the non-rigid and articulated motion of human faces. Parametric flow models (for example affine) are popular for estimating motion in rigid scenes. We observe that within local regions in space and time, such models not only accurately model non-rigid(More)
This paper addresses the problem of capturing the dynamics for exemplar-based recognition systems. Traditional HMM provides a probabilistic tool to capture system dynamics and in exemplar paradigm, HMM states are typically coupled with the exemplars. Alternatively, we propose a non-parametric HMM approach that uses a discrete HMM with arbitrary states(More)
In this paper a radial basis function network architecture is developed that learns the correlation of facial feature motion patterns and human expressions. We describe a hierarchical approach which at the highest level identifies expressions, at the mid level determines motion of facial features, and at the low level recovers motion directions. Individual(More)
A framework for learning parameterized models of optical flow from image sequences is presented. A class of motions is represented by a set of orthogonal basis flow fields that are computed from a training set using principal component analysis. Many complex image motions can be represented by a linear combination of a small number of these basis flows. The(More)
We propose a generalized model of image \appear-ance change" in which brightness variation over time is represented as a probabilistic mixture of diierent causes. We deene four generative models of appearance change due to: 1) object or camera motion; 2) illumination phenomena; 3) specular reeections; and 4) \iconic changes" which are speciic to the objects(More)