Modelling and recognition of the linguistic components in American Sign Language
Image and Vision Computing
Human-inspired search for redundancy in automatic sign language recognition
ACM Transactions on Applied Perception (TAP)
Spherical embedding and classification
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
Multifactor feature extraction for human movement recognition
Computer Vision and Image Understanding
Gesture recognition by stereo vision
Proceedings of the First International Conference on Intelligent Interactive Technologies and Multimedia
An experimental study of one- and two-level classifier fusion for different sample sizes
Pattern Recognition Letters
Online hand gesture recognition using surface electromyography based on flexible neural trees
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part III
Influence of handshape information on automatic sign language recognition
GW'09 Proceedings of the 8th international conference on Gesture in Embodied Communication and Human-Computer Interaction
The dissimilarity representation for structural pattern recognition
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Multimedia Tools and Applications
One-shot learning gesture recognition from RGB-D data using bag of features
The Journal of Machine Learning Research
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To recognize speech, handwriting or sign language, many hybrid approaches have been proposed that combine Dynamic Time Warping (DTW) or Hidden Markov Models (HMM) with discriminative classifiers. However, all methods rely directly on the likelihood models of DTW/HMM. We hypothesize that time warping and classification should be separated because of conflicting likelihood modelling demands. To overcome these restrictions, we propose to use Statistical DTW (SDTW) only for time warping, while classifying the warped features with a different method. Two novel statistical classifiers are proposed (CDFD and Q-DFFM), both using a selection of discriminative features (DF), and are shown to outperform HMM and SDTW. However, we have found that combining likelihoods of multiple models in a second classification stage degrades performance of the proposed classifiers, while improving performance with HMM and SDTW. A proof-of-concept experiment, combining DFFM mappings of multiple SDTW models with SDTW likelihoods, shows that also for model-combining, hybrid classification can provide significant improvement over SDTW. Although recognition is mainly based on 3D hand motion features, these results can be expected to generalize to recognition with more detailed measurements such as hand/body pose and facial expression.