The DOI matches its Crossref record; 49 of the 49 references it cites were found in a registry or an index.
FaceBoxes: A CPU real-time face detector with high accuracy
Open accessAbstract
Although tremendous strides have been made in face detection, one of the remaining open challenges is to achieve real-time speed on the CPU as well as maintain high performance, since effective models for face detection tend to be computationally prohibitive. To address this challenge, we propose a novel face detector, named FaceBoxes, with superior performance on both speed and accuracy. Specifically, our method has a lightweight yet powerful network structure that consists of the Rapidly Digested Convolutional Layers (RDCL) and the Multiple Scale Convolutional Layers (MSCL). The RDCL is designed to enable FaceBoxes to achieve real-time speed on the CPU. The MSCL aims at enriching the receptive fields and discretizing anchors over different layers to handle faces of various scales. Besides, we propose a new anchor densification strategy to make different types of anchors have the same density on the image, which significantly improves the recall rate of small faces. As a consequence, the proposed detector runs at 20 FPS on a single CPU core and 125 FPS using a GPU for VGA-resolution images. Moreover, the speed of FaceBoxes is invariant to the number of faces. We comprehensively evaluate this method and present state-of-the-art detection performance on several face detection benchmark datasets, including the AFW, PASCAL face, and FDDB. Code is available at https://github.com/sfzhang15/FaceBoxes
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Cites
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
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Going deeper with convolutions
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(2016) · Cited by 31,590
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Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks
Zhang, K., Zhang, Z., Li, Z., & Qiao, Y. (2016) · IEEE Signal Processing Letters · doi:10.1109/lsp.2016.2603342 · Cited by 6,214
Neural network-based face detection
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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
Self-citationUnitBox: An Advanced Object Detection Network
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Going Deeper with Convolutions
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Cited by
Single-Shot Refinement Neural Network for Object Detection
Zhang, S., Wen, L., Bian, X., Lei, Z., & Li, S. Z. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00442 · Cited by 1,678
Self-citationA review of object detection based on deep learning
Xiao, Y., Tian, Z., Yu, J., Zhang, Y., Liu, S., Du, S., & Lan, X. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-08976-6 · Cited by 584
Occlusion-Aware R-CNN: Detecting Pedestrians in a Crowd
Zhang, S., Wen, L., Bian, X., Lei, Z., & Li, S. Z. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01219-9_39 · Cited by 488
Self-citationYolo-facev2: A scale and occlusion aware face detector
Yu, Z., Huang, H., Chen, W., Su, Y., Liu, Y., & Wang, X. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110714 · Cited by 476
Self-citationPyramidBox: A Context-Assisted Single Shot Face Detector
Tang, X., Du, D. K., He, Z., & Liu, J. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01240-3_49 · Cited by 426
Metadata
- Authors
- Zhang, S., Zhu, X., Lei, Z., Shi, H., Wang, X., & Li, S. Z.
- Venue
- 2017 IEEE International Joint Conference on Biometrics (IJCB)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 1-9
- OpenAlex
- W2964325361
- Cited by
- 293
- References
- 49
- FWCI
- 8.17
- Type
- conference paper
- Language
- en
- Open access
- green
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- Zhang, S., Zhu, X., Lei, Z., Shi, H., Wang, X., & Li, S. Z. (2017). FaceBoxes: A CPU real-time face detector with high accuracy. 2017 IEEE International Joint Conference on Biometrics (IJCB). https://doi.org/10.1109/btas.2017.8272675
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