A convolutional neural network cascade for face detection
Abstract
In real-world face detection, large visual variations, such as those due to pose, expression, and lighting, demand an advanced discriminative model to accurately differentiate faces from the backgrounds. Consequently, effective models for the problem tend to be computationally prohibitive. To address these two conflicting challenges, we propose a cascade architecture built on convolutional neural networks (CNNs) with very powerful discriminative capability, while maintaining high performance. The proposed CNN cascade operates at multiple resolutions, quickly rejects the background regions in the fast low resolution stages, and carefully evaluates a small number of challenging candidates in the last high resolution stage. To improve localization effectiveness, and reduce the number of candidates at later stages, we introduce a CNN-based calibration stage after each of the detection stages in the cascade. The output of each calibration stage is used to adjust the detection window position for input to the subsequent stage. The proposed method runs at 14 FPS on a single CPU core for VGA-resolution images and 100 FPS using a GPU, and achieves state-of-the-art detection performance on two public face detection benchmarks.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Cascaded detection has been especially applied to detection tasks of small objects in large scenes, e.g., face detection and pedestrian detection.
“In recent years, cascaded detection has been especially applied to those detection tasks of 'small objects in large scenes', e.g., face detection [126, 127], pedestrian detection [101, 124, 128], etc.”
From Object Detection in 20 Years: A Survey (2023) · cites this paper as [126]
Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380 · see citing works
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684 · see citing works
The pascal visual object classes (VOC) challenge
Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-009-0275-4 · Cited by 19,599 · see citing works
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 · see citing works
Caffe: Convolutional Architecture for Fast Feature Embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654889 · Cited by 11,077 · see citing works
Object Detection with Discriminatively Trained Part-Based Models
Felzenszwalb, P. F., Girshick, R. B., McAllester, D., & Ramanan, D. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.167 · Cited by 9,853 · see citing works
Convolutional networks for images, speech, and time series
LeCun, Y., & Bengio, Y. (1998) · HAL · doi:10.5555/303568.303704 · Cited by 4,637 · see citing works
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 1× · see citing works
“Since the seminal Viola-Jones face detector [27], a number of variants are proposed for real-time face detection [10, 17, 29, 30].”
BackgroundFace detection, pose estimation, and landmark localization in the wild
Zhu, X., & Ramanan, D. (2012) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2012.6248014 · Cited by 2,171 · see citing works
Köstinger, M., Wohlhart, P., Roth, P. M., & Bischof, H. (2011) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2011.6130513 · Cited by 1,004 · see citing works
FDDB: A benchmark for face detection in unconstrained settings
Jain, V., & Miller, E. G. (2010) · ScholarWorks@UMassAmherst (University of Massachusetts Amherst) · Cited by 879 · see citing works
Fast Multi-view Face Detection
Jones, M., & Viola, P. A. (2003) · CTIT technical reports series · Cited by 615 · see citing works
A Survey of Recent Advances in Face Detection
Zhang, C., & Zhang, Z. (2010) · Cited by 511 · see citing works
Face Detection without Bells and Whistles
Mathias, M., Benenson, R., Pedersoli, M., & Gool, L. V. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10593-2_47 · Cited by 505 · see citing works
Coarse-to-Fine Auto-Encoder Networks (CFAN) for Real-Time Face Alignment
Zhang, J., Shan, S., Kan, M., & Chen, X. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10605-2_1 · Cited by 479 · see citing works
Rotation Invariant Neural Network-Based Face Detection
Rowley, H. A., Baluja, S., & Takeo Kanade, A. (2005) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.1998.698721 · Cited by 471 · see citing works
Joint Cascade Face Detection and Alignment
Chen, D., Ren, S., Wei, Y., Cao, X., & Sun, J. Z. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10599-4_8 · Cited by 411 · see citing works
Neural network-based face detection
Rowley, H. A., Baluja, S., & Takeo Kanade, A. (1996) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.1996.517075 · Cited by 332 · see citing works
Aggregate channel features for multi-view face detection
Yang, B., Yan, J., Lei, Z., & Li, S. Z. (2014) · IEEE International Joint Conference on Biometrics (IJCB) · doi:10.1109/btas.2014.6996284 · Cited by 286 · see citing works
Original approach for the localisation of objects in images
Vaillant, R. (1994) · IEE Proceedings - Vision, Image, and Signal Processing · doi:10.1049/ip-vis:19941301 · Cited by 280 · see citing works
Rotation Invariant Neural Network-Based Face Detection
Rowley, H. A., Baluja, S., & Kanade, T. (1997) · doi:10.21236/ada341629 · Cited by 253 · see citing works
Multiresolution Models for Object Detection
Park, D., Ramanan, D., & Fowlkes, C. C. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15561-1_18 · Cited by 232 · see citing works
Synergistic Face Detection and Pose Estimation with Energy-Based Models
Osadchy, M., LeCun, Y., & Miller, M. L. (2006) · Lecture notes in computer science · doi:10.1007/11957959_10 · Cited by 211 · see citing works
Face detection by structural models
Yan, J., Zhang, X., Lei, Z., & Li, S. Z. (2013) · Image and Vision Computing · doi:10.1016/j.imavis.2013.12.004 · Cited by 193 · see citing works
Online domain adaptation of a pre-trained cascade of classifiers
Jain, V., & Learned-Miller, E. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995317 · Cited by 161 · see citing works
Detecting and Aligning Faces by Image Retrieval
Shen, X., Lin, Z., Brandt, J., & Wu, Y. N. (2013) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2013.444 · Cited by 149 · see citing works
Self-citationReal-time Accurate Object Detection using Multiple Resolutions
Zhang, W., Zelinsky, G. J., & Samaras, D. (2007) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2007.4409057 · Cited by 125 · see citing works
Face detection using SURF cascade
Li, J., Wang, T., & Zhang, Y. (2011) · IEEE International Conference on Computer Vision · doi:10.1109/iccvw.2011.6130518 · Cited by 117 · see citing works
Efficient Boosted Exemplar-Based Face Detection
Li, H., Lin, Z., Brandt, J., et al. (2014) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2014.238 · Cited by 106 · see citing works
Self-citationProbabilistic Elastic Part Model for Unsupervised Face Detector Adaptation
Li, H., Hua, G., Lin, Z., et al. (2013) · IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.103 · Cited by 88 · see citing works
Self-citationA neural architecture for fast and robust face detection
García, C., & Delakis, M. (2003) · doi:10.1109/icpr.2002.1048232 · Cited by 75 · see citing works
A Method for Object Detection Based on Pixel Intensity Comparisons Organized in Decision TreesPreprint
Markuš, N., Frljak, M., Pandzic, I. S., Ahlberg, J., & Forchheimer, R. (2013) · arXiv (Cornell University) · Cited by 20 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Cascade R-CNN: Delving Into High Quality Object Detection
Cai, Z., & Vasconcelos, N. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00644 · Cited by 7,370 · see citing works
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 · see citing works
Object Detection in 20 Years: A Survey
Zou, Z., Chen, K., Shi, Z., Guo, Y., & Ye, J. (2023) · Proceedings of the IEEE · doi:10.1109/jproc.2023.3238524 · Cited by 3,689 · see citing works
“, face detection [126], [127] and pedestrian detection [101], [124], [128].”
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 · see citing works
UnitBox: An Advanced Object Detection Network
Yu, J., Jiang, Y., Wang, Z., Cao, Z., & Huang, T. (2016) · Proceedings of the 24th ACM international conference on Multimedia · doi:10.1145/2964284.2967274 · Cited by 1,731 · see citing works
Hybrid Task Cascade for Instance Segmentation
Chen, K., Pang, J., Wang, J., Xiong, Y., Li, X., Sun, S., Feng, W., Liu, Z., Shi, J., Ouyang, W., et al. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00511 · Cited by 1,587 · see citing works
Perceptual Generative Adversarial Networks for Small Object Detection
Li, J., Liang, X., Wei, Y., Xu, T., Feng, J., & Yan, S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.211 · Cited by 953 · see citing works
“Some efforts [4, 25, 18, 39, 23, 1] have been devoted to addressing small object detection problems.”
BackgroundDynamic Neural Networks: A Survey
Han, Y., Huang, G., Song, S., Yang, L., Wang, H., & Wang, Y. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3117837 · Cited by 848 · see citing works
Zhang, J., & Tao, D. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3039359 · Cited by 774 · see citing works
Identifying facial phenotypes of genetic disorders using deep learning
Gurovich, Y., Hanani, Y., Bar, O., Nadav, G., Fleischer, N., Gelbman, D., Basel-Salmon, L., Krawitz, P. M., Kamphausen, S. B., Zenker, M., et al. (2019) · Nature Medicine · doi:10.1038/s41591-018-0279-0 · Cited by 739 · see citing works
An on-chip photonic deep neural network for image classification
Ashtiani, F., Geers, A. J., & Aflatouni, F. (2022) · Nature · doi:10.1038/s41586-022-04714-0 · Cited by 699 · see citing works
Automatic Detection of Cerebral Microbleeds From MR Images via 3D Convolutional Neural Networks
Dou, Q., Chen, H., Yu, L., Zhao, L., Qin, J., Wang, D., Mok, V. C., Shi, L., & Heng, P.-A. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2528129 · Cited by 679 · see citing works
Yang, F., Choi, W., & Lin, Y. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.234 · Cited by 608 · see citing works
A 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 · see citing works
Yolo-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 · see citing works
NoScope: optimizing neural network queries over video at scale
Kang, D., Emmons, J., Abuzaid, F., Bailis, P., & Zaharia, M. (2017) · Proceedings of the VLDB Endowment · doi:10.14778/3137628.3137664 · Cited by 367 · see citing works
AI Benchmark: Running Deep Neural Networks on Android Smartphones
Ignatov, A., Timofte, R., Chou, W., Wang, K., Wu, M., Hartley, T., & Van Gool, L. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-11021-5_19 · Cited by 347 · see citing works
Pre-trained convolutional neural networks as feature extractors for tuberculosis detection
Lopes, U. K., & Valiati, J. F. (2017) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2017.08.001 · Cited by 294 · see citing works
Revisiting RCNN: On Awakening the Classification Power of Faster RCNN
Cheng, B., Wei, Y., Shi, H., Feris, R., Xiong, J., & Huang, T. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01267-0_28 · Cited by 282 · see citing works
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 · see citing works
Supervised Transformer Network for Efficient Face Detection
Chen, D., Hua, G., Wen, F., & Sun, J. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46454-1_8 · Cited by 185 · see citing works
Self-citationFacial Sentiment Analysis Using AI Techniques: State-of-the-Art, Taxonomies, and Challenges
Patel, K., Mehta, D., Mistry, C., Gupta, R., Tanwar, S., Kumar, N., & Alazab, M. (2020) · IEEE Access · doi:10.1109/access.2020.2993803 · Cited by 157 · see citing works
Edge Video Analytics for Public Safety: A Review
Zhang, Q., Sun, H., Wu, X., & Zhong, H. (2019) · Proceedings of the IEEE · doi:10.1109/jproc.2019.2925910 · Cited by 156 · see citing works
Dynamic Zoom-in Network for Fast Object Detection in Large Images
Gao, M., Yu, R., Li, A., Morariu, V. I., & Davis, L. S. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00724 · Cited by 148 · see citing works
Pan, B., Hirota, K., Jia, Z., & Dai, Y. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126866 · Cited by 142 · see citing works
Direct face detection and video reconstruction from event cameras
Miyatani, Y., Barua, S., & Veeraraghavan, A. (2016) · 2016 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv.2016.7477561 · Cited by 133 · see citing works
Hao, Z., Liu, Y., Qin, H., Yan, J., Li, X., & Hu, X. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.207 · Cited by 134 · see citing works
Real-Time Rotation-Invariant Face Detection with Progressive Calibration Networks
Shi, X., Shan, S., Kan, M., Wu, S., & Chen, X. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00244 · Cited by 105 · see citing works
Race Recognition Using Deep Convolutional Neural Networks
Vo, T., Nguyen, T., & Le, C. T. (2018) · Symmetry · doi:10.3390/sym10110564 · Cited by 59 · see citing works
Yang, S., Wang, W., Liu, C., Deng, W., & Hedrick, J. K. (2017) · 2017 IEEE Intelligent Vehicles Symposium (IV) · doi:10.1109/ivs.2017.7995850 · Cited by 54 · see citing works
Face Recognition Based on Deep Learning and FPGA for Ethnicity Identification
AlBdairi, A. J. A., Xiao, Z., Alkhayyat, A., Humaidi, A. J., Fadhel, M. A., Taher, B. H., Alzubaidi, L., Santamaría, J., & Al-Shamma, O. (2022) · Applied Sciences · doi:10.3390/app12052605 · Cited by 54 · see citing works
FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance
Chen, L., Zaharia, M., & Zou, J. (2023) · arXiv · doi:10.48550/arxiv.2305.05176 · Cited by 51 · see citing works
“The concept of using multiple services for speed is known as model cascade Viola & Jones (2004), which has been applied in predictive ML domains such as pedestrian detection Cai et al. (2015) and facial recognition Li et al. (2015); Sun et al. (2013).”
Finizola, J. S., Targino, J. M., Teodoro, F. G. S., & de Moraes Lima, C. A. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-03928-8_18 · Cited by 9 · see citing works
SAFER: Situation Aware Facial Emotion Recognition
Palash, M., & Bhargava, B. (2023) · arXiv · doi:10.48550/arxiv.2306.09372 · Cited by 2 · see citing works
“Various techniques have been proposed for this task, such as the Viola-Jones method [10, 11], the combination of PCA and Viola-Jones [12], and the Haar Cascades method [13].”
Method
- Authors
- Li, H., Lin, Z., Shen, X., Brandt, J., & Hua, G.
- Affiliations
- Stevens Institute of Technology (US)
- Adobe Systems (United States) (US)
- Venue
- 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2015-06
- Pages
- 5325-5334
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W1934410531
- Cited by
- 1,390
- References
- 33
- Citations by year
- 2026: 14 · 2025: 50 · 2024: 72 · 2023: 90 · 2022: 120 · 2021: 176 · 2020: 203 · 2019: 220 · 2018: 208 · 2017: 137 · 2016: 92 · 2015: 8
- FWCI
- 60.83
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- Funders
- National Science Foundation
- National Institute of Nursing Research
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Face Recognition and Perception
- Keywords
- face detection, background suppression, real-time face detection, pose and expression variation
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
