The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Hidden Conditional Random Fields for Gesture Recognition
Abstract
We introduce a discriminative hidden-state approach for the recognition of human gestures. Gesture sequences often have a complex underlying structure, and models that can incorporate hidden structures have proven to be advantageous for recognition tasks. Most existing approaches to gesture recognition with hidden states employ a Hidden Markov Model or suitable variant (e.g., a factored or coupled state model) to model gesture streams; a significant limitation of these models is the requirement of conditional independence of observations. In addition, hidden states in a generative model are selected to maximize the likelihood of generating all the examples of a given gesture class, which is not necessarily optimal for discriminating the gesture class against other gestures. Previous discriminative approaches to gesture sequence recognition have shown promising results, but have not incorporated hidden states nor addressed the problem of predicting the label of an entire sequence. In this paper, we derive a discriminative sequence model with a hidden state structure, and demonstrate its utility both in a detection and in a multi-way classification formulation. We evaluate our method on the task of recognizing human arm and head gestures, and compare the performance of our method to both generative hidden state and discriminative fully-observable models.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A tutorial on hidden Markov models and selected applications in speech recognition
Rabiner, L. R. (1989) · Proceedings of the IEEE · doi:10.1109/5.18626 · Cited by 22,743
(1988) · Cited by 16,582
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
Lafferty, J., McCallum, A., & Pereira, F. C. N. (2001) · ScholarlyCommons (University of Pennsylvania) · Cited by 12,935
Visual interpretation of hand gestures for human-computer interaction: a review
Pavlovic, V. I., Sharma, R., & Huang, T. S. (1997) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.598226 · Cited by 1,791
Maximum Entropy Markov Models for Information Extraction and Segmentation
McCallum, A., Freitag, D., & Pereira, F. C. N. (2000) · Cited by 1,315
McCallum, A., & Li, W. (2003) · Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - · doi:10.3115/1119176.1119206 · Cited by 1,160
Coupled hidden Markov models for complex action recognition
Brand, M., Oliver, N., & Pentland, A. (2002) · Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.1997.609450 · Cited by 1,052
Real-time American Sign Language recognition from video using hidden Markov models
Starner, T., & Pentland, A. (2002) · Proceedings of International Symposium on Computer Vision - ISCV · doi:10.1109/iscv.1995.477012 · Cited by 773
Kumar, S., & Hebert (2003) · Proceedings Ninth IEEE International Conference on Computer Vision · doi:10.1109/iccv.2003.1238478 · Cited by 427
Conditional random fields for object recognition
Quattoni, A., Collins, M., & Darrell, T. (2004) · Cited by 342
Self-citation
Cited by
Hand gesture recognition with 3D convolutional neural networks
Molchanov, P., Gupta, S., Kim, K., & Kautz, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2015.7301342 · Cited by 543
Conditional random fields for activity recognition
Vail, D. L., Veloso, M. M., & Lafferty, J. D. (2007) · Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems · doi:10.1145/1329125.1329409 · Cited by 357
Car that Knows Before You Do: Anticipating Maneuvers via Learning Temporal Driving Models
Jain, A., Koppula, H. S., Raghavan, B., Soh, S., & Saxena, A. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.364 · Cited by 274
Multimodal Gesture Recognition Based on the ResC3D Network
Miao, Q., Li, Y., Ouyang, W., Ma, Z., Xu, X., Shi, W., & Cao, X. (2017) · 2017 IEEE International Conference on Computer Vision Workshops (ICCVW) · doi:10.1109/iccvw.2017.360 · Cited by 168
Walecki, R., Rudovic, O., Pavlovic, V., & Pantic, M. (2015) · 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG) · doi:10.1109/fg.2015.7163137 · Cited by 65
Metadata
- Authors
- Wang, S. B., Quattoni, A., Morency, L.-P., Demirdjian, D., & Darrell, T.
- Venue
- 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06)
- Publisher
- IEEE
- Published
- 2006-07-10
- Volume
- 2
- Pages
- 1521-1527
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2145835757
- Cited by
- 451
- References
- 21
- FWCI
- 16.53
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Hidden Conditional Random Fields for Gesture Recognition. https://app.cochat.ai/papers/10.1109/cvpr.2006.132
- The paper
- Wang, S. B., Quattoni, A., Morency, L.-P., Demirdjian, D., & Darrell, T. (2006). Hidden Conditional Random Fields for Gesture Recognition. 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06). https://doi.org/10.1109/cvpr.2006.132
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
