The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Learning temporal features using LSTM-CNN architecture for face anti-spoofing
Abstract
Temporal features is important for face anti-spoofing. Unfortunately existing methods have limitations to explore such temporal features. In this work, we propose a deep neural network architecture combining Long Short-Term Memory (LSTM) units with Convolutional Neural Networks (CNN). Our architecture works well for face anti-spoofing by utilizing the LSTM units' ability of finding long relation from its input sequences as well as extracting local and dense features through convolution operations. Our best model shows significant performance improvement over general CNN architecture (5.93% vs. 7.34%), and hand-crafted features (5.93% vs. 10.00%) on CASIA dataset.
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
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
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
Beyond short snippets: Deep networks for video classification
Ng, J. Y.-H., Hausknecht, M., Vijayanarasimhan, S., Vinyals, O., Monga, R., & Toderici, G. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7299101 · Cited by 2,062
(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
Self-citationFace spoofing detection from single images using micro-texture analysis
Maatta, J., Hadid, A., & Pietikainen, M. (2011) · 2011 International Joint Conference on Biometrics (IJCB) · doi:10.1109/ijcb.2011.6117510 · Cited by 679
Eyeblink-based Anti-Spoofing in Face Recognition from a Generic Webcamera
Pan, G., Sun, L., Wu, Z., & Lao, S. (2007) · 2007 IEEE 11th International Conference on Computer Vision · doi:10.1109/iccv.2007.4409068 · Cited by 658
Learn Convolutional Neural Network for Face Anti-Spoofing
Yang, J., Lei, Z., & Li, S. Z. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1408.5601 · Cited by 356
Self-citationLBP − TOP Based Countermeasure against Face Spoofing Attacks
de Freitas Pereira, T., Anjos, A., De Martino, J. M., & Marcel, S. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-37410-4_11 · Cited by 319
Cited by
Face anti-spoofing using patch and depth-based CNNs
Atoum, Y., Liu, Y., Jourabloo, A., & Liu, X. (2017) · 2017 IEEE International Joint Conference on Biometrics (IJCB) · doi:10.1109/btas.2017.8272713 · Cited by 429
Single-Side Domain Generalization for Face Anti-Spoofing
Jia, Y., Zhang, J., Shan, S., & Chen, X. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00851 · Cited by 313
Deep 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
Deep 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
- Xu, Z., Li, S., & Deng, W.
- Venue
- 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR)
- Publisher
- IEEE
- Published
- 2015-11
- Pages
- 141-145
- OpenAlex
- W2409050142
- Cited by
- 293
- References
- 19
- FWCI
- 6.46
- 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: Learning temporal features using LSTM-CNN architecture for face anti-spoofing. https://app.cochat.ai/papers/10.1109/acpr.2015.7486482
- The paper
- Xu, Z., Li, S., & Deng, W. (2015). Learning temporal features using LSTM-CNN architecture for face anti-spoofing. 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR). https://doi.org/10.1109/acpr.2015.7486482
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.
