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WIDER FACE: A Face Detection Benchmark
Open accessAbstract
Face detection is one of the most studied topics in the computer vision community. Much of the progresses have been made by the availability of face detection benchmark datasets. We show that there is a gap between current face detection performance and the real world requirements. To facilitate future face detection research, we introduce the WIDER FACE dataset1, which is 10 times larger than existing datasets. The dataset contains rich annotations, including occlusions, poses, event categories, and face bounding boxes. Faces in the proposed dataset are extremely challenging due to large variations in scale, pose and occlusion, as shown in Fig. 1. Furthermore, we show that WIDER FACE dataset is an effective training source for face detection. We benchmark several representative detection systems, providing an overview of state-of-the-art performance and propose a solution to deal with large scale variation. Finally, we discuss common failure cases that worth to be further investigated.
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“To obtain the accuracy metric values for the models, we consider the following 4 datasets: (i) PASCAL VOC (20 object categories) [6], (ii) SKU-110k (1 class, retail item detection) [9], (iii) WIDER FACE (1 class, face detection) [35], (iv) COCO (80 object categories) [21].”
MethodSong, X., Zhang, Y., Lu, Z., Xu, L., & Shen, H. (2026) · Computers & Education · doi:10.1016/j.compedu.2026.105578 · Cited by 12
Vidanaralage, J., Dharmaratne, A., & Haque, S. (2022) · Australasian Journal of Educational Technology · doi:10.14742/ajet.7473 · Cited by 8
“The SSD MobileNet model is pretrained using the WIDERFACE public dataset (Yang et al., 2016), consisting of 32,203+ images and 393,703+ labelled faces.”
MethodChen, Z., Lu, L., Yuan, Z., Zhu, Y., Li, Y., Yuan, C., & Deng, W. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i2.27889 · Cited by 3
Metadata
- Authors
- Yang, S., Luo, P., Loy, C. C., & Tang, X.
- Venue
- 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2016-06
- Pages
- 5525-5533
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2963566548
- Cited by
- 1,830
- References
- 4
- FWCI
- 45.81
- Type
- conference paper
- Language
- en
- Open access
- green
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