The DOI matches its Crossref record; 47 of the 48 references it cites were found in a registry or an index.
The TUM Gait from Audio, Image and Depth (GAID) database: Multimodal recognition of subjects and traits
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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?
In the citing paper
“The TUM Gait from Audio, Image, and Depth (GAID) [33] database, as the name suggests, is compounded by RGB images, audio, and depth, recorded from 305 people in three different variations at first.”
From Gait Recognition Based on Deep Learning: A Survey (2023) · cites this paper as [33]
In the citing paper
“A strong advantage of this dataset is a well-defined evaluation protocol.”
From Gait Recognition Based on Deep Learning: A Survey (2023) · cites this paper as [33]
Citations
Cites
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584
The WEKA data mining software: an update
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1656274.1656278 · Cited by 17,750
The random subspace method for constructing decision forests
Ho, T. K. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.709601 · Cited by 7,005
Adaptive background mixture models for real-time tracking
Stauffer, C., & Grimson, W. E. L. (2003) · 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.1999.784637 · Cited by 6,925
Region Filling and Object Removal by Exemplar-Based Image Inpainting
Criminisi, A., Perez, P., & Toyama, K. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2004.833105 · Cited by 3,258 · cited 1×
“based on inpainting [42] or based on filter techniques [43] can be used to efficiently fill the holes.”
MethodOpensmile: the munich versatile and fast open-source audio feature extractor
Eyben, F., Wöllmer, M., & Schuller, B. (2010) · Proceedings of the 18th ACM international conference on Multimedia · doi:10.1145/1873951.1874246 · Cited by 2,642
Self-citationIndividual recognition using gait energy image
Han, J., & Bhanu, B. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.38 · Cited by 1,873
General Tensor Discriminant Analysis and Gabor Features for Gait Recognition
Tao, D., Li, X., Wu, X., & Maybank, S. J. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1096 · Cited by 1,140
The humanID gait challenge problem: data sets, performance, and analysis
Sarkar, S., Phillips, P. J., Liu, Z., Vega, I. R., Grother, P., & Bowyer, K. W. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.39 · Cited by 1,133
Yu, S., Tan, D., & Tan, T. (2006) · 18th International Conference on Pattern Recognition (ICPR'06) · doi:10.1109/icpr.2006.67 · Cited by 1,056
Research works printed but not linked: 1
- B. She, Framework of footstep detection in in-door environment, in: Proceedings of International Congress on Acoustics, 2004, pp. 715–718.
Cited by
Sorted by citations
GEINet: View-invariant gait recognition using a convolutional neural network
Shiraga, K., Makihara, Y., Muramatsu, D., Echigo, T., & Yagi, Y. (2016) · 2016 International Conference on Biometrics (ICB) · doi:10.1109/icb.2016.7550060 · Cited by 468
Gait Recognition via Disentangled Representation Learning
Zhang, Z., Tran, L., Yin, X. P., Atoum, Y., Liu, X., Wan, J., & Wang, N. (2019) · 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.2019.00484 · Cited by 302
(2019) · ACM Computing Surveys · doi:10.1145/3230633 · Cited by 303
Biometric recognition by gait: A survey of modalities and features
Connor, P. C., & Ross, A. A. (2018) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2018.01.007 · Cited by 272
Vision-Based Gait Recognition: A Survey
Singh, J. P., Jain, S., Arora, S., & Singh, U. P. (2018) · IEEE Access · doi:10.1109/access.2018.2879896 · Cited by 192
Metadata
- Authors
- Hofmann, M., Geiger, J., Bachmann, S., Schuller, B., & Rigoll, G.
- Affiliations
- Technical University of Munich (DE)
- Corresponding author
- Hofmann, Martin
- Venue
- Journal of Visual Communication and Image Representation
- Publisher
- Elsevier BV
- Published
- 2014-01
- Volume
- 25
- Issue
- 1
- Pages
- 195-206
- ISSN
- 1047-3203 (ISSN-L)
- OpenAlex
- W1984031350
- Cited by
- 246
- References
- 48
- Citations by year
- 2026: 13 · 2025: 22 · 2024: 19 · 2023: 22 · 2022: 33 · 2021: 27 · 2020: 14 · 2019: 19 · 2018: 23 · 2017: 13 · 2016: 11 · 2015: 16 · 2014: 10
- FWCI
- 7.08
- Type
- journal article
- Language
- en
- Open access
- Licence
- cc-by-nc-nd
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- Locations
- Journal of Visual Communication and Image Representation (publishedVersion)
- OPUS (Augsburg University) (submittedVersion, cc-by-nc-nd)
- Funders
- ALIAS
- EC
- French ANR
- German BMBF
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Keywords
- gait recognition, multimodal fusion, depth video, audio, RGB video, personal identification, soft biometrics, age estimation, gender prediction, stature estimation, temporal variation
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.
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