Visual Tracking and Recognition Using Probabilistic Appearance Manifolds
Abstract
This paper presents an algorithm for modelling, tracking, and
recognizing human faces in video sequences within one integrated
framework. Conventional video-based face recognition systems have usually
been embodied with two independent components: the tracking and
recognition modules. In contrast, our algorithm emphasizes an algorithmic architecture
that tightly couples these two components within one single
framework. This is accomplished through a novel appearance model which is
utilized simultaneously by both modules, even with their disparate
requirements and functions. The complex nonlinear appearance manifold of each registered
person is partitioned into a collection of sub-manifolds where each models the face appearances of the person in nearby poses. The submanifold is approximated by a low-dimensional linear
subspace computed by principal component analysis using images sampled from training video sequences. The connectivity between the submanifolds is modeled as transition
probabilities between pairs of submanifolds, and these are learned directly from training video sequences. The integrated task of tracking and recognition is formulated as a
maximum a posteriori estimation problem. Within our framework, the tracking and recognition modules are
complementary to each other, and the capability and performance of
one are enhanced by the other. Our approach contrasts sharply with more rigid conventional
approaches in which these two modules work independently and in
sequence. We report on a number of experiments and results that demonstrate
the robustness, effectiveness and stability of our algorithm.
Visual Tracking and Recognition Using Probabilistic Appearance Manifolds [ pdf ]
Kuang-Chih Lee, Jeffrey Ho, Ming-Hsuan Yang, David Kriegman / Submitted to Computer Vision and Image Understanding (CVIU), 2004
Video-Based Face Recognition Using Probabilistic Appearance Manifolds [ pdf ]
Kuang-Chih Lee, Jeffrey Ho, Ming-Hsuan Yang, David Kriegman / IEEE Conf. On Computer Vision and Pattern Recognition, pp. 313-320, vol. 1, 2003