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Feature Detection and Tracking with Constrained Local Models
Abstract
We present an efficient and robust model matching method which uses a joint shape and texture appearance model to generate a set of region template detectors. The model is fitted to an unseen image in an iterative manner by generating templates using the joint model and the current parameter estimates, correlating the templates with the target image to generate response images and optimising the shape parameters so as to maximise the sum of responses. The appearance model is similar to that used in the AAM [1]. However in our approach the appearance model is used to generate likely feature templates, instead of trying to approximate the image pixels directly. We show that when applied to human faces, our Constrained Local Model (CLM) algorithm is more robust and more accurate than the original AAM search method, which relies on the image reconstruction error to update the model parameters. We demonstrate improved localisation accuracy on two publicly available face data sets and improved tracking on a challenging set of in-car face sequences. 1
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Cited by
Face detection, pose estimation, and landmark localization in the wild
Zhu, X., & Ramanan, D. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6248014 · Cited by 2,120
Bulat, A., & Tzimiropoulos, G. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.116 · Cited by 1,464
Face Alignment at 3000 FPS via Regressing Local Binary Features
Ren, S., Cao, X., Wei, Y., & Sun, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.218 · Cited by 853
Deformable Model Fitting by Regularized Landmark Mean-Shift
Saragih, J. M., Lucey, S., & Cohn, J. F. (2011) · International Journal of Computer Vision · doi:10.1007/s11263-010-0380-4 · Cited by 787
300 Faces In-The-Wild Challenge: Database and results
Sagonas, C., Antonakos, E., Tzimiropoulos, G., Zafeiriou, S., & Pantic, M. (2016) · Image and Vision Computing · doi:10.1016/j.imavis.2016.01.002 · Cited by 638
Metadata
- Authors
- Cristinacce, D., & Cootes, T. F.
- Venue
- Procedings of the British Machine Vision Conference 2006
- Publisher
- British Machine Vision Association
- Published
- 2006
- Pages
- 95.1-95.10
- OpenAlex
- W1977821862
- Cited by
- 718
- References
- 0
- FWCI
- 15.55
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Cristinacce, D., & Cootes, T. F. (2006). Feature Detection and Tracking with Constrained Local Models. Procedings of the British Machine Vision Conference 2006. https://doi.org/10.5244/c.20.95
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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.
