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International Journal of Computer Vision
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
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EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation
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International Journal of Computer Vision
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Symmetric Shape-from-Shading Using Self-ratio Image
International Journal of Computer Vision
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Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Face Recognition: Features Versus Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation
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Face Identification by Fitting a 3D Morphable Model Using Linear Shape and Texture Error Functions
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ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Face Recognition from Long-Term Observations
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Automatic Video-based Person Authentication Using the RBF Network
AVBPA '97 Proceedings of the First International Conference on Audio- and Video-Based Biometric Person Authentication
A Dynamic Bayesian Network Approach to Tracking Using Learned Switching Dynamic Models
HSCC '00 Proceedings of the Third International Workshop on Hybrid Systems: Computation and Control
Elliptical Head Tracking Using Intensity Gradients and Color Histograms
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
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FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
Interpreting Face Images Using Active Appearance Models
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Video-Based Online Face Recognition Using Identity Surfaces
RATFG-RTS '01 Proceedings of the IEEE ICCV Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems (RATFG-RTS'01)
Mixtures of Eigenfeatures for Real-Time Structure from Texture
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
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ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
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AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
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CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
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Computer Vision and Image Understanding
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VNBA '08 Proceedings of the 1st ACM workshop on Vision networks for behavior analysis
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Machine Vision and Applications
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Joint trajectory tracking and recognition based on bi-directional nonlinear learning
Image and Vision Computing
Tensor distance based multilinear multidimensional scaling for image and video analysis
MM '09 Proceedings of the 17th ACM international conference on Multimedia
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This paper presents an algorithm for modeling, 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 a 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 submanifolds 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 subman-ifolds 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.