An HMM-Based Threshold Model Approach for Gesture Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
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
Signer-Independent Continuous Sign Language Recognition Based on SRN/HMM
GW '01 Revised Papers from the International Gesture Workshop on Gesture and Sign Languages in Human-Computer Interaction
A Real-Time Large Vocabulary Continuous Recognition System for Chinese Sign Language
PCM '01 Proceedings of the Second IEEE Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Relevant Features for Video-Based Continuous Sign Language Recognition
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
A framework for motion recognition with applications to American sign language and gait recognition
HUMO '00 Proceedings of the Workshop on Human Motion (HUMO'00)
An Approach Based on Phonemes to Large Vocabulary Chinese Sign Language Recognition
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
A SRN/HMM System for Signer-Independent Continuous Sign Language Recognition
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Rapid Signer Adaptation for Isolated Sign Language Recognition
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Sign Language Spotting with a Threshold Model Based on Conditional Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing Spatiotemporal Gestures and Movement Epenthesis in Sign Language
IMVIP '09 Proceedings of the 2009 13th International Machine Vision and Image Processing Conference
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sign Language Phoneme Transcription with Rule-based Hand Trajectory Segmentation
Journal of Signal Processing Systems
Robust Sign Language Recognition with Hierarchical Conditional Random Fields
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Deciphering gestures with layered meanings and signer adaptation
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
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This paper presents a segment-based probabilistic approach to robustly recognize continuous sign language sentences. The recognition strategy is based on a two-layer conditional random field (CRF) model, where the lower layer processes the component channels and provides outputs to the upper layer for sign recognition. The continuously signed sentences are first segmented, and the sub-segments are labeled SIGN or ME (movement epenthesis) by a Bayesian network (BN) which fuses the outputs of independent CRF and support vector machine (SVM) classifiers. The sub-segments labeled as ME are discarded and the remaining SIGN sub-segments are merged and recognized by the two-layer CRF classifier; for this we have proposed a new algorithm based on the semi-Markov CRF decoding scheme. With eight signers, we obtained a recall rate of 95.7% and a precision of 96.6% for unseen samples from seen signers, and a recall rate of 86.6% and a precision of 89.9% for unseen signers.