The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Understanding images of groups of people
Abstract
In many social settings, images of groups of people are captured. The structure of this group provides meaningful context for reasoning about individuals in the group, and about the structure of the scene as a whole. For example, men are more likely to stand on the edge of an image than women. Instead of treating each face independently from all others, we introduce contextual features that encapsulate the group structure locally (for each person in the group) and globally (the overall structure of the group). This “social context” allows us to accomplish a variety of tasks, such as such as demographic recognition, calculating scene and camera parameters, and even event recognition. We perform human studies to show this context aids recognition of demographic information in images of strangers.
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
Active Shape Models-Their Training and Application
Cootes, T. F., Taylor, C. J., Cooper, D. H., & Graham, J. (1995) · Computer Vision and Image Understanding · doi:10.1006/cviu.1995.1004 · Cited by 7,190
The Environment and Social Behavior: Privacy, Personal Space, Territory, Crowding.
Gove, W. R., & Altman, I. (1978) · Contemporary Sociology · doi:10.2307/2065073 · Cited by 1,299
Shotton, J., Winn, J., Rother, C., & Criminisi, A. (2006) · Lecture Notes in Computer Science · doi:10.1007/11744023_1 · Cited by 1,132
A System for the Notation of Proxemic Behavior1
HALL, E. T. (1963) · American Anthropologist · doi:10.1525/aa.1963.65.5.02a00020 · Cited by 1,006
Putting Objects in Perspective
Hoiem, D., Efros, A. A., & Hebert, M. (2008) · International Journal of Computer Vision · doi:10.1007/s11263-008-0137-5 · Cited by 805
Image-Based Human Age Estimation by Manifold Learning and Locally Adjusted Robust Regression
Guo, G., Fu, Y., Dyer, C. R., & Huang, T. S. (2008) · IEEE Transactions on Image Processing · doi:10.1109/tip.2008.924280 · Cited by 645
Criminisi, A., Reid, I., & Zisserman, A. (2000) · International Journal of Computer Vision · doi:10.1023/a:1026598000963 · Cited by 618
Comparing Different Classifiers for Automatic Age Estimation
Lanitis, A., Draganova, C., & Christodoulou, C. (2004) · IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) · doi:10.1109/tsmcb.2003.817091 · Cited by 569
Object categorization using co-occurrence, location and appearance
Galleguillos, C., Rabinovich, A., & Belongie, S. (2008) · 2008 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2008.4587799 · Cited by 457
Boosting Sex Identification Performance
Baluja, S., & Rowley, H. A. (2007) · International Journal of Computer Vision · doi:10.1007/s11263-006-8910-9 · Cited by 350
Cited by
Age Synthesis and Estimation via Faces: A Survey
Fu, Y., Guo, G., & Huang, T. S. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.36 · Cited by 569
Describing Clothing by Semantic Attributes
Chen, H., Gallagher, A., & Girod, B. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33712-3_44 · Cited by 411
Self-citationThe more the merrier: Analysing the affect of a group of people in images
Dhall, A., Joshi, J., Sikka, K., Goecke, R., & Sebe, N. (2015) · 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG) · doi:10.1109/fg.2015.7163151 · Cited by 93
Metadata
- Authors
- Gallagher, A. C., & Chen, T.
- Venue
- 2009 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2009-06
- Pages
- 256-263
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2135964285
- Cited by
- 351
- References
- 24
- FWCI
- 13.09
- 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: Understanding images of groups of people. https://app.cochat.ai/papers/10.1109/cvpr.2009.5206828
- The paper
- Gallagher, A. C., & Chen, T. (2009). Understanding images of groups of people. 2009 IEEE Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2009.5206828
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.