Real-Time American Sign Language Recognition Using Desk and Wearable Computer Based Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
A framework for recognizing the simultaneous aspects of American sign language
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
Extraction of 2D Motion Trajectories and Its Application to Hand Gesture Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Towards an Automatic Sign Language Recognition System Using Subunits
GW '01 Revised Papers from the International Gesture Workshop on Gesture and Sign Languages in Human-Computer Interaction
A Novel Approach to Automatically Extracting Basic Units from Chinese Sign Language
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Multilayer architecture in sign language recognition system
Proceedings of the 6th international conference on Multimodal interfaces
Recognition of sign language subwords based on boosted hidden Markov models
ICMI '05 Proceedings of the 7th international conference on Multimodal interfaces
A Unified System for Segmentation and Tracking of Face and Hands in Sign Language Recognition
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Modelling and segmenting subunits for sign language recognition based on hand motion analysis
Pattern Recognition Letters
Fourier Descriptors for Plane Closed Curves
IEEE Transactions on Computers
Sharing features: efficient boosting procedures for multiclass object detection
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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Recently, a promising research direction has emerged in sign language recognition (SLR) aimed at breaking up signs into manageable subunits. This paper presents a novel SL learning technique based on boosted subunits. Three main contributions distinguish the proposed work from traditional approaches: 1) A novel boosting framework is developed to recognize SL. The learning is based on subunits instead of the whole sign, which is more scalable for the recognition task. 2) Feature selection is performed to learn a small set of discriminative combinations of subunits and SL features. 3) A joint learning strategy is adopted to share subunits across sign classes, which leads to a better performance classifiers. Our experiments show that compared to Dynamic Time Warping (DTW) when applied on the whole sign, our proposed technique gives better results.