Face and facial feature extraction from color image
Abstract
This paper presents an automatic processing of human face from color images. The system works hierarchically from detecting the position of human face and its features (such as eyes, nose, mouth, etc.) to contours and feature points extraction. The position of human face and its parts are detected from the image by applying the integral projection method, which synthesize the color information (skin and hair color) and the edge information (intensity and sign). In order to extract the contour-line of face features we used a multiple active contour model with color information based energy terms. Facial feature points are decided based on the optimized contours. The proposed system is confirmed to be very effective and robust to deal with the image of faces in the complex background.
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?
Facial expressions help coordinate conversation.
“Considerable research in social psychology has also shown that facial expressions help coordinate conversation [4], [82], and have considerably more effect on whether a listener feels liked or disliked than the speaker's spoken words [55].”
From Automatic analysis of facial expressions: the state of the art (2000) · cites this paper as [4]
Cites
Kass, M., Witkin, A., & Terzopoulos, D. (1988) · International Journal of Computer Vision · doi:10.1007/bf00133570 · Cited by 17,165 · see citing works
Face recognition: features versus templates
Brunelli, R., & Poggio, T. (1993) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.254061 · Cited by 2,452 · see citing works
Feature extraction from faces using deformable templates
Yuille, A., Hallinan, P. W., & Cohen, D. S. (1992) · International Journal of Computer Vision · doi:10.1007/bf00127169 · Cited by 1,373 · 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
Face detection by fuzzy pattern matching
Chen, Q., Wu, H., & Yachida, M. (2002) · IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.1995.466885 · Cited by 121 · see citing works
Self-citationRobust active contours with insensitive parameters
Xu, G., Segawa, E., & Tsuji, S. (1994) · Pattern Recognition · doi:10.1016/0031-3203(94)90153-8 · Cited by 90 · see citing works
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
- Authors
- Wu, H., Yokoyama, T., Pramadihanto, D., & Yachida, M.
- Affiliations
- The University of Osaka (JP)
- Venue
- Proceedings of the Second International Conference on Automatic Face and Gesture Recognition
- Publisher
- IEEE Comput. Soc. Press
- Published
- 2002-12-24
- Pages
- 345-350
- ISSN
- 1541-5058 (ISSN-L)
- OpenAlex
- W2143279446
- Cited by
- 55
- References
- 6
- Citations by year
- 2023: 1 · 2021: 3 · 2020: 1 · 2018: 1 · 2016: 1 · 2015: 2 · 2014: 5 · 2013: 1 · 2012: 2
- FWCI
- 3.41
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Automatic Face and Gesture Recognition (publishedVersion)
- 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
- Life Sciences › Neuroscience › Cognitive Neuroscience › Face Recognition and Perception
- Keywords
- face detection, facial feature extraction, color images, snakes, facial landmark detection, complex background
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
