Sign Language Recognition by Combining Statistical DTW and Independent Classification

To recognize speech, handwriting, or sign language, many hybrid approaches have been proposed that combine dynamic time warping (DTW) or hidden Markov models (HMMs) with discriminative classifiers. However, all methods rely directly on the likelihood models of DTW/HMM. We hypothesize that time warpi...

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Veröffentlicht in:IEEE transactions on pattern analysis and machine intelligence Jg. 30; H. 11; S. 2040 - 2046
Hauptverfasser: Lichtenauer, J.F., Hendriks, E.A., Reinders, M.J.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Los Alamitos, CA IEEE 01.11.2008
IEEE Computer Society
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
3D/stereo scene analysis
Algorithms
Applied sciences
Artificial Intelligence
Classification
Classifier design and evaluation
Classifiers
Computer science; control theory; systems
Computer vision
Computing Methodology
Data Interpretation, Statistical
Degradation
Detectors
Distortion
Exact sciences and technology
Face and gesture recognition
Face recognition
Feature recognition
Handicapped aids
Handwriting recognition
Hidden Markov models
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Information Storage and Retrieval - methods
Mapping
Markov processes
Mathematical models
Pattern Recognition, Automated - methods
Pattern recognition. Digital image processing. Computational geometry
Real-time systems
Recognition
Shape
Sign language
Speech and sound recognition and synthesis. Linguistics
Speech recognition
Time series analysis
Vision and Scene Understanding
Warpage
Warping
Time series analysis
Artificial Intelligence
Markov processes
Vision and Scene Understanding
Real-time systems
Computing Methodology
Face and gesture recognition
Classifier design and evaluation
3D/stereo scene analysis
Markov process
Warping
classifier design and evaluation
statistical dynamic programming
Modeling
Classification
Facies
Hybrid model
Dynamic programming
Stereopsis
Pattern analysis
Time analysis
Multimodel
Statistical analysis
Probabilistic approach
Gesture recognition
Time series
Markov model
Real time system
face and gesture recognition
Sign language
Scene analysis
real-time systems
Hidden Markov model
Manuscript character
Artificial intelligence
Facial expression
ISSN:0162-8828, 1939-3539, 1939-3539
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Zusammenfassung:To recognize speech, handwriting, or sign language, many hybrid approaches have been proposed that combine dynamic time warping (DTW) or hidden Markov models (HMMs) 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 modeling demands. To overcome these restrictions, we propose using statistical DTW (SDTW) only for time warping, while classifying the warped features with a different method. Two novel statistical classifiers are proposed - combined discriminative feature detectors (CDFDs) and quadratic classification on DF Fisher mapping (Q-DFFM) - both using a selection of discriminative features (DFs), 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.
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ISSN:0162-8828
1939-3539
1939-3539
DOI:10.1109/TPAMI.2008.123