Real-Time American Sign Language Recognition Using Desk and Wearable Computer Based Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Framework for Robust Subspace Learning
International Journal of Computer Vision - Special Issue on Computational Vision at Brown University
High breakdown estimators for principal components: the projection-pursuit approach revisited
Journal of Multivariate Analysis
R1-PCA: rotational invariant L1-norm principal component analysis for robust subspace factorization
ICML '06 Proceedings of the 23rd international conference on Machine learning
Transformation Invariance in Hand Shape Recognition
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Journal of Cognitive Neuroscience
Principal Component Analysis Based on L1-Norm Maximization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Vision-Based Hand Gesture Recognition Using PCA+Gabor Filters and SVM
IIH-MSP '09 Proceedings of the 2009 Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing
Large lexicon detection of sign language
HCI'07 Proceedings of the 2007 IEEE international conference on Human-computer interaction
A boosted classifier tree for hand shape detection
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
The Journal of Machine Learning Research
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Hand modeling and tracking are essential in video-based sign language recognition. The high reformability and the large number of degrees of freedom of hands render the problem difficult. To tackle these challenges, a novel approach based on robust principal component analysis (PCA) is proposed. The robust PCA incorporates an L1 norm objective function to deal with background clutter, and a projection pursuit strategy to deal with the lack of alignment due to the deformation of hands. The learning algorithm of the robust PCA is very simple, involving only a search for the solutions in a finite set constructed from the training data, which leads to the learning of much more representative and interpretable bases. The incorporation of the L1 regularization in the fitting of the learned robust PCA models results in cleaner reconstructions and more stable fitting. Based on the robust PCA, a hand tracking system is developed that contains a skin-color region segmentation based on graph cuts and template matching in the framework of particle filtering. Experiments on a publicly available sign-language video database demonstrates the strength of the method.