The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Example-based learning for view-based human face detection
Open accessAbstract
We present an example-based learning approach for locating vertical frontal views of human faces in complex scenes. The technique models the distribution of human face patterns by means of a few view-based "face" and "nonface" model clusters. At each image location, a difference feature vector is computed between the local image pattern and the distribution-based model. A trained classifier determines, based on the difference feature vector measurements, whether or not a human face exists at the current image location. We show empirically that the distance metric we adopt for computing difference feature vectors, and the "nonface" clusters we include in our distribution-based model, are both critical for the success of our system.
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Cites
Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Foundations
Rumelhart, D. E., McClelland, J. L., & AU (1986) · doi:10.7551/mitpress/5236.001.0001 · Cited by 15,686
Turk, M., & Pentland, A. (1991) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.1991.3.1.71 · Cited by 13,733
(1973) · CERN Document Server (European Organization for Nuclear Research) · Cited by 12,895
Application of the Karhunen-Loeve procedure for the characterization of human faces
Kirby, M., & Sirovich, L. (1990) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.41390 · Cited by 2,448
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 2,436
Self-citationView-based and modular eigenspaces for face recognition
Pentland, Moghaddam, & Starner (1994) · Proceedings of IEEE Conference on Computer Vision and Pattern Recognition CVPR-94 · doi:10.1109/cvpr.1994.323814 · Cited by 1,800
Feature extraction from faces using deformable templates
Yuille, A. L., Hallinan, P. W., & Cohen, D. S. (1992) · International Journal of Computer Vision · doi:10.1007/bf00127169 · Cited by 1,369
Pattern Classification and Scene Analysis.
Grenander, U., Duda, R. O., & Hart, P. E. (1974) · Journal of the American Statistical Association · doi:10.2307/2286028 · Cited by 1,006
Pedestrian detection using wavelet templates
Oren, M., Papageorgiou, C., Sinha, P., Osuna, E., & Poggio, T. (2002) · Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.1997.609319 · Cited by 695
Self-citationModel-Based Recognition and Localization from Sparse Range or Tactile Data
Grimson, W. E. L., & Lozano-Pérez, T. (1984) · The International Journal of Robotics Research · doi:10.1177/027836498400300301 · Cited by 586
Cited by
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684
Rapid object detection using a boosted cascade of simple features
Viola, P., & Jones, M. (2005) · Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001 · doi:10.1109/cvpr.2001.990517 · Cited by 18,300
Robust Real-Time Face Detection
Viola, P., & Jones, M. J. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000013087.49260.fb · Cited by 14,097
YOLOv4: Optimal Speed and Accuracy of Object Detection
Bochkovskiy, A., Wang, C.-Y., & Liao, H.-Y. M. (2020) · arXiv · doi:10.48550/arxiv.2004.10934 · Cited by 10,307
Object Detection with Discriminatively Trained Part-Based Models
Felzenszwalb, P. F., Girshick, R. B., McAllester, D., & Ramanan, D. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.167 · Cited by 9,853
Metadata
- Authors
- Sung, K.-K., & Poggio, T.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 1998
- Volume
- 20
- Issue
- 1
- Pages
- 39-51
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2159686933
- Cited by
- 1,773
- References
- 15
- FWCI
- 85.70
- Type
- journal article
- Language
- en
- Open access
- green
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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.
