Learn 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 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 f r a m e w o r k f o r m o d e l i n g a n d r e c o g n i t i o n o f t e m p o r a l a c t i v i t i e s is proposed . T h e m o d e l i n g o f s e t s o f e x e m p l a r a c t i v i t i e s is ach ieved by p a r a m e t e r i z i n g t h e i r r e p r e s e n t a t i o n in t h e f o r m o f p r i n c i p a l c o m p o n e n t s . R e c o g n i t i(More)
In this paper we extend the work of Black and Yacoob [5] on tracking and recognition of human facial expressions to the problem of tracking and recognizing the articulated motion of human limbs. We make the assumption that a person can be represented by a set of connected planar patches: the cardboard person model illustrated in Figure 1. In the case of(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)
We propose a generalized model of image “appearance change” in which brightness variation over time is represented as a probabilistic mixture of different causes. We define four generative models of appearance change due to (1) object or camera motion; (2) illumination phenomena; (3) specular reflections; and (4) “iconic changes” which are specific to the(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)
An approach for estimating 3D head orientation in a monocular image sequence is proposed. The approach employs recently developed image-based parameterized tracking for face and face features to locate the area in which a sub-pixel parameter-ized shape estimation of the eye's boundary is performed. This involves tracking of ve points (four at the eye(More)