The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
Face anti-spoofing using patch and depth-based CNNs
Abstract
The face image is the most accessible biometric modality which is used for highly accurate face recognition systems, while it is vulnerable to many different types of presentation attacks. Face anti-spoofing is a very critical step before feeding the face image to biometric systems. In this paper, we propose a novel two-stream CNN-based approach for face anti-spoofing, by extracting the local features and holistic depth maps from the face images. The local features facilitate CNN to discriminate the spoof patches independent of the spatial face areas. On the other hand, holistic depth map examine whether the input image has a face-like depth. Extensive experiments are conducted on the challenging databases (CASIA-FASD, MSU-USSA, and Replay Attack), with comparison to the state of the art.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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
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
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., et al. (2016) · arXiv · doi:10.48550/arxiv.1603.04467 · Cited by 9,408
Indoor segmentation and support inference from rgbd images
Silberman, N., Hoiem, D., Kohli, P., & Fergus, R. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33715-4_54 · Cited by 5,760
A Convolutional Neural Network for Modelling Sentences
Kalchbrenner, N., Grefenstette, E., & Blunsom, P. (2014) · Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.3115/v1/p14-1062 · Cited by 3,601
Face recognition: a convolutional neural-network approach
Lawrence, S., Giles, C. L., Tsoi, A. C., & Back, A. D. (1997) · IEEE Transactions on Neural Networks · doi:10.1109/72.554195 · Cited by 3,092
Face recognition based on fitting a 3D morphable model
Blanz, V., & Vetter, T. (2003) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2003.1227983 · Cited by 2,002
Disentangled Representation Learning GAN for Pose-Invariant Face Recognition
Tran, L., Yin, X., & Liu, X. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.141 · Cited by 1,005
Self-citation(2012) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · Cited by 826
A face antispoofing database with diverse attacks
Zhang, Z., Yan, J., Liu, S., Lei, Z., Yi, D., & Li, S. Z. (2012) · 2012 5th IAPR International Conference on Biometrics (ICB) · doi:10.1109/icb.2012.6199754 · Cited by 816
Cited by
Density-Aware Single Image De-raining Using a Multi-stream Dense Network
Zhang, H., & Patel, V. M. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00079 · Cited by 1,066
On the Detection of Digital Face Manipulation
Dang, H., Liu, F., Stehouwer, J., Liu, X., & Jain, A. K. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00582 · Cited by 764
Self-citationDeep Tree Learning for Zero-Shot Face Anti-Spoofing
Liu, Y., Stehouwer, J., Jourabloo, A., & Liu, X. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00481 · Cited by 278
Self-citationDeep learning for face anti-spoofing: A survey
Yu, Z., Qin, Y., Li, X., Zhao, C., Lei, Z., & Zhao, G. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3215850 · Cited by 227
Metadata
- Authors
- Atoum, Y., Liu, Y., Jourabloo, A., & Liu, X.
- Venue
- 2017 IEEE International Joint Conference on Biometrics (IJCB)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 319-328
- OpenAlex
- W2787613668
- Cited by
- 429
- References
- 42
- FWCI
- 26.63
- Type
- conference paper
- 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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Face anti-spoofing using patch and depth-based CNNs. https://app.cochat.ai/papers/10.1109/btas.2017.8272713
- The paper
- Atoum, Y., Liu, Y., Jourabloo, A., & Liu, X. (2017). Face anti-spoofing using patch and depth-based CNNs. 2017 IEEE International Joint Conference on Biometrics (IJCB). https://doi.org/10.1109/btas.2017.8272713
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.
