The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
High-Performance Rotation Invariant Multiview Face Detection
Abstract
Rotation invariant multiview face detection (MVFD) aims to detect faces with arbitrary rotation-in-plane (RIP) and rotation-off-plane (ROP) angles in still images or video sequences. MVFD is crucial as the first step in automatic face processing for general applications since face images are seldom upright and frontal unless they are taken cooperatively. In this paper, we propose a series of innovative methods to construct a high-performance rotation invariant multiview face detector, including the Width-First-Search (WFS) tree detector structure, the Vector Boosting algorithm for learning vector-output strong classifiers, the domain-partition-based weak learning method, the sparse feature in granular space, and the heuristic search for sparse feature selection. As a result of that, our multiview face detector achieves low computational complexity, broad detection scope, and high detection accuracy on both standard testing sets and real-life images.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A decision-theoretic generalization of on-line learning and an application to boosting
Freund, Y., & Schapire, R. E. (1997) · Journal of Computer and System Sciences · doi:10.1006/jcss.1997.1504 · Cited by 20,466
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
Friedman, J., Hastie, T., & Tibshirani, R. (2000) · The Annals of Statistics · doi:10.1214/aos/1016218223 · Cited by 6,884
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
An extended set of Haar-like features for rapid object detection
Lienhart, R., & Maydt, J. (2003) · Proceedings. International Conference on Image Processing · doi:10.1109/icip.2002.1038171 · Cited by 2,977 · cited 3×
“Therefore, compared with the Haar-like features [7] as well as their extended versions in [9], [18], the sparse granular features are highly scalable: they can be more versatile while keeping the same computation load, or more economic to compute if keeping similar structural complexity.”
BackgroundMethodImproved boosting algorithms using confidence-rated predictions
Schapire, R. E., & Singer, Y. (1998) · Proceedings of the eleventh annual conference on Computational learning theory · doi:10.1145/279943.279960 · Cited by 2,547
Training support vector machines: an application to face detection
Osuna, E., Freund, R., & Girosit, F. (2002) · Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.1997.609310 · Cited by 2,486
Boosting the margin: a new explanation for the effectiveness of voting methods
Bartlett, P., Freund, Y., Lee, W. S., & Schapire, R. E. (1998) · The Annals of Statistics · doi:10.1214/aos/1024691352 · Cited by 2,306
Improved Boosting Algorithms Using Confidence-rated Predictions
Schapire, R. E., & Singer, Y. (1999) · Machine Learning · doi:10.1023/a:1007614523901 · Cited by 1,971
Boosting a Weak Learning Algorithm by Majority
Freund, Y. (1995) · Information and Computation · doi:10.1006/inco.1995.1136 · Cited by 1,674
Cited by
RepPoints: Point Set Representation for Object Detection
Yang, Z., Liu, S., Hu, H., Wang, L., & Lin, S. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00975 · Cited by 1,307
Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges
Ding, J., Xue, N., Xia, G.-S., Bai, X., Yang, W., Yang, M. Y., Belongie, S., Luo, J., Datcu, M., Pelillo, M., et al. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3117983 · Cited by 678
Multi-view Face Detection Using Deep Convolutional Neural Networks
Farfade, S. S., Saberian, M. J., & Li, L.-J. (2015) · Proceedings of the 5th ACM on International Conference on Multimedia Retrieval · doi:10.1145/2671188.2749408 · Cited by 572
Detecting Masked Faces in the Wild with LLE-CNNs
Ge, S., Li, J., Ye, Q., & Luo, Z. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.53 · Cited by 454
Joint Cascade Face Detection and Alignment
Chen, D., Ren, S., Wei, Y., Cao, X., & Sun, J. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10599-4_8 · Cited by 405
Metadata
- Authors
- Huang, C., Ai, H., Li, Y., & Lao, S.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2007-04
- Volume
- 29
- Issue
- 4
- Pages
- 671-686
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- OpenAlex
- W2169696215
- Cited by
- 366
- References
- 36
- FWCI
- 42.77
- Type
- journal article
- Language
- en
- Open access
- closed
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: High-Performance Rotation Invariant Multiview Face Detection. https://app.cochat.ai/papers/10.1109/tpami.2007.1011
- The paper
- Huang, C., Ai, H., Li, Y., & Lao, S. (2007). High-Performance Rotation Invariant Multiview Face Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/tpami.2007.1011
Data sources
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.
