Tracking and recognizing rigid and non-rigid facial motions using local parametric models of image motion
Abstract
This paper explores the use of local parametrized models of image motion for recovering and recognizing the non-rigid and articulated motion of human faces. Parametric flow models (for example affine) are popular for estimating motion in rigid scenes. We observe that within local regions in space and time, such models not only accurately model non-rigid facial motions but also provide a concise description of the motion in terms of a small number of parameters. These parameters are intuitively related to the motion of facial features during facial expressions and we show how expressions such as anger, happiness, surprise, fear, disgust and sadness can be recognized from the local parametric motions in the presence of significant head motion. The motion tracking and expression recognition approach performs with high accuracy in extensive laboratory experiments involving 40 subjects as well as in television and movie sequences.>
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The approaches explored lately include systems for automatic analysis and synthesis of facial expressions.
“The approaches that have been explored lately also include systems for automatic analysis and synthesis of facial expressions [86], [56], [39], [47], [89], [88], [53], [14], [19]”
From Automatic analysis of facial expressions: the state of the art (2000) · cites this paper as [56]
Cites
Ekman, P. (1975) · Cited by 1,565 · see citing works
Feature extraction from faces using deformable templates
Yuille, A. L., Cohen, D. S., & Hallinan, P. W. (2003) · Proceedings CVPR '89: IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.1989.37836 · Cited by 708 · see citing works
Analysis and synthesis of facial image sequences using physical and anatomical models
Terzopoulos, D., & Waters, K. (1993) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.216726 · Cited by 538 · see citing works
A framework for the robust estimation of optical flow
Black, M. J., & Anandan, P. (2002) · International Conference on Computer Vision · doi:10.1109/iccv.1993.378214 · Cited by 456 · see citing works
Self-citationAzarbayejani, A. J., Starner, T. E., Horowitz, B., et al. (1993) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.216730 · Cited by 185 · see citing works
Computing Spatio-Temporal Representations of Human Faces
Yacoob, Y., & Davis, L. S. (1994) · Cited by 158 · see citing works
Self-citationComputing spatio-temporal representations of human faces
Yacoob, & Davis (1994) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.1994.323812 · Cited by 141 · see citing works
A Vision System for Observing and Extracting Facial Action Parameters
Essa, I. A., & Pentland, A. (1994) · Cited by 129 · see citing works
A vision system for observing and extracting facial action parameters
Essa, & Pentland (1994) · doi:10.1109/cvpr.1994.323813 · Cited by 99 · see citing works
Essa, I. A., Darrell, T. J., & Pentland, A. (2002) · doi:10.1109/mnrao.1994.346257 · Cited by 79 · see citing works
Labeling of Human Face Components from Range Data
Yacoob, Y., & Davis, L. S. (1994) · CVGIP Image Understanding · doi:10.1006/ciun.1994.1045 · Cited by 39 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Automatic analysis of facial expressions: the state of the art
Pantie, M., & Rothkrantz, L. J. M. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.895976 · Cited by 1,778 · see citing works
Recognizing action units for facial expression analysis
Tian, Y.-I., Kanade, T., & Cohn, J. F. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.908962 · Cited by 1,745 · see citing works
A review of affective computing: From unimodal analysis to multimodal fusion
Poria, S., Cambria, E., Bajpai, R., & Hussain, A. (2017) · Information Fusion · doi:10.1016/j.inffus.2017.02.003 · Cited by 1,644 · see citing works
Efficient region tracking with parametric models of geometry and illumination
Hager, G. D., & Belhumeur, P. N. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.722606 · Cited by 1,066 · see citing works
Analysis of emotion recognition using facial expressions, speech and multimodal information
Busso, C., Deng, Z., Yildirim, S., Bulut, M., Lee, C. M., Kazemzadeh, A., Lee, S., Neumann, U., & Narayanan, S. (2004) · Proceedings of the 6th international conference on Multimodal interfaces · doi:10.1145/1027933.1027968 · Cited by 840 · see citing works
Video Rewrite: driving visual speech with audio
Bregler, C., Covell, M., & Slaney, M. (1997) · Proceedings of the 24th annual conference on Computer graphics and interactive techniques - SIGGRAPH '97 · doi:10.1145/258734.258880 · Cited by 669 · see citing works
Communication via eye blinks and eyebrow raises: video-based human-computer interfaces
Grauman, K., Betke, M., Lombardi, J., Gips, J., & Bradski, G. R. (2003) · Universal Access in the Information Society · doi:10.1007/s10209-003-0062-x · Cited by 185 · see citing works
Spotting segments displaying facial expression from image sequences using HMM
Otsuka, T., & Ohya, J. (2002) · Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition · doi:10.1109/afgr.1998.670988 · Cited by 69 · see citing works
- Authors
- Black, M. J., & Yacoob, Y.
- Affiliations
- Palo Alto Research Center (US)
- ORCID
- Black, M.J.
- Venue
- Proceedings of IEEE International Conference on Computer Vision
- Publisher
- IEEE Comput. Soc. Press
- Published
- 2002-11-19
- Pages
- 374-381
- OpenAlex
- W2109269337
- Cited by
- 457
- References
- 11
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 1 · 2023: 1 · 2022: 8 · 2021: 9 · 2020: 3 · 2019: 6 · 2018: 10 · 2017: 11 · 2016: 12 · 2015: 13 · 2014: 17 · 2013: 15 · 2012: 9
- FWCI
- 34.11
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- Funders
- Defense Advanced Research Projects Agency
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Keywords
- facial motion tracking, facial expression recognition, articulated motion, head movement, video sequences
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
