The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Detecting human faces in color images
Abstract
We propose a new method to detect human faces in color images. A human skin color model is built to capture the chromatic properties based on multivariate statistical analysis. Given a color image, multiscale segmentation is used to generate homogeneous regions at multiple different scales. From the coarsest to the finest scale, regions of skin color are merged until the shape is approximately elliptic. Postprocessing is performed to determine whether a merged region contains a human face and include the facial features of non-skin color such as eyes and mouth if necessary. Experimental results show that human faces in color images can be detected regardless of size, orientation and viewpoint.
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Citations
Cites
Segmentation and tracking of faces in color images
Sobottka, K., & Pitas, I. (2002) · Proceedings of the Second International Conference on Automatic Face and Gesture Recognition · doi:10.1109/afgr.1996.557270 · Cited by 157
Cited by
Detecting faces in images: a survey
Yang, M.-H., Kriegman, D. J., & Ahuja, N. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.982883 · Cited by 3,427
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Metadata
- Authors
- Yang, M.-H., & Ahuja, N.
- Venue
- Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269)
- Publisher
- IEEE Comput. Soc
- Published
- 2002-11-27
- Volume
- 1
- Pages
- 127-130
- ISSN
- 1522-4880 (ISSN-L)
- OpenAlex
- W2131035963
- Cited by
- 212
- References
- 1
- FWCI
- 15.08
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Yang, M.-H., & Ahuja, N. (2002). Detecting human faces in color images. Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269). https://doi.org/10.1109/icip.1998.723442
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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.
