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Detecting faces in images: a survey
Abstract
Images containing faces are essential to intelligent vision-based human-computer interaction, and research efforts in face processing include face recognition, face tracking, pose estimation and expression recognition. However, many reported methods assume that the faces in an image or an image sequence have been identified and localized. To build fully automated systems that analyze the information contained in face images, robust and efficient face detection algorithms are required. Given a single image, the goal of face detection is to identify all image regions which contain a face, regardless of its 3D position, orientation and lighting conditions. Such a problem is challenging because faces are non-rigid and have a high degree of variability in size, shape, color and texture. Numerous techniques have been developed to detect faces in a single image, and the purpose of this paper is to categorize and evaluate these algorithms. We also discuss relevant issues such as data collection, evaluation metrics and benchmarking. After analyzing these algorithms and identifying their limitations, we conclude with several promising directions for future research.
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Detection and localization of faces on digital images
Burel, G., & Carel, D. (1994) · Pattern Recognition Letters · doi:10.1016/0167-8655(94)90027-2 · Cited by 69
Neural Network-Based Face Detection
Rowley, H. A. (1999) · Cited by 69
Face detection based on color and local symmetry information
Sun, Q., Huang, W., & Wu, J. K. (2002) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/afgr.1998.670937 · Cited by 63
Detecting human faces in color images
Cai, J., Ardeshir Goshtasby, A., & Yu, C. (2002) · International Workshop on Multi-Media Database Management Systems · doi:10.1109/mmdbms.1998.709526 · Cited by 61
Object oriented face detection using range and color information
Kim, S.-H., Kim, N.-K., Ahn, S. C., & Kim, H.-G. (2002) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/afgr.1998.670928 · Cited by 60
Face and facial feature extraction from color image
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Integration and control of reactive visual processes
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Fast algorithm for locating head boundaries
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Fast face detection via morphology-based pre-processing
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Detection of human faces using decision trees
Huang, J. R., Gutta, S., & Wechsler, H. (2002) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/afgr.1996.557272 · Cited by 46
Locating faces using statistical feature detectors
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LISTEN: a system for locating and tracking individual speakers
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Extraction of Facial Images from the Complex Background Using Color Information and SGLD Matrices
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An image database for benchmarking of automatic face detection and recognition algorithms
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Enhancing human face detection using motion and active contours
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Zero crossings of a non-orthogonal wavelet transform for object location
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Deep Learning for Generic Object Detection: A Survey
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Deep Learning for Generic Object Detection: A Survey
Liu, L., Ouyang, W., Wang, X., Fieguth, P., Chen, J., Liu, X., & Pietikäinen, M. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-019-01247-4 · Cited by 2,965
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
Content-based multimedia information retrieval: State of the art and challenges
Lew, M. S., Sebe, N., Djeraba, C., & Jain, R. (2006) · ACM Transactions on Multimedia Computing, Communications, and Applications · doi:10.1145/1126004.1126005 · Cited by 1,528
Multimodal human–computer interaction: A survey
Jaimes, A., & Sebe, N. (2007) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2006.10.019 · Cited by 965
Metadata
- Authors
- Yang, M.-H., Kriegman, D. J., & Ahuja, N.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2002
- Volume
- 24
- Issue
- 1
- Pages
- 34-58
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2121601095
- Cited by
- 3,427
- References
- 151
- FWCI
- 218.66
- 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
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.
