The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
A survey on face detection in the wild: Past, present and future
Open accessAbstract
We present a comprehensive and survey for face detection 'in-the-wild'.We critically describe the advances in the three main families of algorithms.We comment on the performance of the state-of-the-art in the current benchmarks.We outline future research avenues on the topic and beyond. Face detection is one of the most studied topics in computer vision literature, not only because of the challenging nature of face as an object, but also due to the countless applications that require the application of face detection as a first step. During the past 15years, tremendous progress has been made due to the availability of data in unconstrained capture conditions (so-called 'in-the-wild') through the Internet, the effort made by the community to develop publicly available benchmarks, as well as the progress in the development of robust computer vision algorithms. In this paper, we survey the recent advances in real-world face detection techniques, beginning with the seminal Viola-Jones face detector methodology. These techniques are roughly categorized into two general schemes: rigid templates, learned mainly via boosting based methods or by the application of deep neural networks, and deformable models that describe the face by its parts. Representative methods will be described in detail, along with a few additional successful methods that we briefly go through at the end. Finally, we survey the main databases used for the evaluation of face detection algorithms and recent benchmarking efforts, and discuss the future of face detection.
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Cites
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MethodInfluential
Cited by
Deep Learning for Generic Object Detection: A Survey
Liu, L., Ouyang, W., Wang, X., Fieguth, P., Chen, J., Liu, X., & Pietikäinen, M. (2018) · arXiv · doi:10.48550/arxiv.1809.02165 · Cited by 2,965
Deep Learning for Generic Object Detection: A Survey
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RetinaFace: Single-Shot Multi-Level Face Localisation in the Wild
Deng, J., Guo, J., Ververas, E., Kotsia, I., & Zafeiriou, S. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00525 · Cited by 1,681
Self-citationA survey on deep learning and its applications
Dong, S., Wang, P., & Abbas, K. (2021) · Computer Science Review · doi:10.1016/j.cosrev.2021.100379 · Cited by 1,544
Facial Expression Analysis under Partial Occlusion: A Survey
Zhang, L., Verma, B., Tjondronegoro, D., & Chandran, V. (2019) · ACM Computing Surveys · doi:10.1145/3158369 · Cited by 1,258
Metadata
- Authors
- Zafeiriou, S., Zhang, C., & Zhang, Z.
- Venue
- Computer Vision and Image Understanding
- Publisher
- Elsevier BV
- Published
- 2015-09
- Volume
- 138
- Pages
- 1-24
- ISSN
- 1077-3142 (ISSN-L)
- OpenAlex
- W2160532515
- Cited by
- 458
- References
- 21
- FWCI
- 49.97
- Type
- journal article
- Language
- en
- Open access
- green
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