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 |
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| Sprache: | Englisch |
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Los Alamitos, CA
IEEE
01.11.2008
IEEE Computer Society The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 0162-8828, 1939-3539, 1939-3539 |
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| Abstract | 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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| AbstractList | 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. 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.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. 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. 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 [abstract truncated by publisher]. 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. |
| Author | Hendriks, E.A. Lichtenauer, J.F. Reinders, M.J. |
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| Keywords | 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 |
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| References | ref15 ref14 Gavrila (ref7) Starner (ref6) 1995 ref11 ten Holt (ref13) 2006 ref2 ref17 ref16 ref19 ref18 Sakoe (ref1); 3 van der Kooij (ref12) 2002 ref24 ref23 ref26 Stokoe (ref10) 1960; 8 ref25 ref20 ref22 ref21 ref8 ref9 ref4 ref3 ref5 |
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| SubjectTerms | 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 |
| Title | Sign Language Recognition by Combining Statistical DTW and Independent Classification |
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