The DOI matches its Crossref record; 112 of the 112 references it cites were found in a registry or an index.
Biometric Antispoofing Methods: A Survey in Face Recognition
Open accessAbstract
In recent decades, we have witnessed the evolution of biometric technology from the first pioneering works in face and voice recognition to the current state of development wherein a wide spectrum of highly accurate systems may be found, ranging from largely deployed modalities, such as fingerprint, face, or iris, to more marginal ones, such as signature or hand. This path of technological evolution has naturally led to a critical issue that has only started to be addressed recently: the resistance of this rapidly emerging technology to external attacks and, in particular, to spoofing. Spoofing, referred to by the term presentation attack in current standards, is a purely biometric vulnerability that is not shared with other IT security solutions. It refers to the ability to fool a biometric system into recognizing an illegitimate user as a genuine one by means of presenting a synthetic forged version of the original biometric trait to the sensor. The entire biometric community, including researchers, developers, standardizing bodies, and vendors, has thrown itself into the challenging task of proposing and developing efficient protection methods against this threat. The goal of this paper is to provide a comprehensive overview on the work that has been carried out over the last decade in the emerging field of antispoofing, with special attention to the mature and largely deployed face modality. The work covers theories, methodologies, state-of-the-art techniques, and evaluation databases and also aims at providing an outlook into the future of this very active field of research.
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
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162
From few to many: illumination cone models for face recognition under variable lighting and pose
Georghiades, A. S., Belhumeur, P. N., & Kriegman, D. J. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.927464 · Cited by 4,912
Handbook of Fingerprint Recognition
Maltoni, D., Maio, D., Jain, A. K., & Prabhakar, S. (2009) · Springer eBooks · doi:10.1007/978-1-84882-254-2 · Cited by 2,184
Jain, A. K., Nandakumar, K., & Nagar, A. (2008) · EURASIP Journal on Advances in Signal Processing · doi:10.1155/2008/579416 · Cited by 1,134
Biometrics: A Tool for Information Security
Jain, A. K., Ross, A., & Pankanti, S. (2006) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2006.873653 · Cited by 1,095
Biometric recognition: security and privacy concerns
Prabhakar, S., Pankanti, S., & Jain, A. K. (2003) · IEEE Security & Privacy · doi:10.1109/msecp.2003.1193209 · Cited by 1,091
A survey of approaches and challenges in 3D and multi-modal 3D+2D face recognition
Bowyer, K. W., Chang, K., & Flynn, P. (2006) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2005.05.005 · Cited by 979
(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
Impact of artificial "gummy" fingers on fingerprint systems:
Matsumoto, T., Matsumoto, H., Yamada, K., & Hoshino, S. (2002) · SPIE Proceedings · doi:10.1117/12.462719 · Cited by 775
Cited by
Deepfakes and beyond: A Survey of face manipulation and fake detection
Tolosana, R., Vera-Rodriguez, R., Fierrez, J., Morales, A., & Ortega-Garcia, J. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.06.014 · Cited by 1,237
Presentation Attack Detection Methods for Face Recognition Systems: A Comprehensive Survey
Ramachandra, R., & Busch, C. (2018) · ACM Computing Surveys · doi:10.1145/3038924 · Cited by 332
DeepFake Detection for Human Face Images and Videos: A Survey
Malik, A., Kuribayashi, M., Abdullahi, S. M., & Khan, A. N. (2022) · IEEE Access · doi:10.1109/access.2022.3151186 · Cited by 297
SWAPPED! Digital face presentation attack detection via weighted local magnitude pattern
Agarwal, A., Singh, R., Vatsa, M., & Noore, A. (2017) · 2017 IEEE International Joint Conference on Biometrics (IJCB) · doi:10.1109/btas.2017.8272754 · Cited by 85
GANprintR: Improved Fakes and Evaluation of the State of the Art in Face Manipulation Detection
Neves, J. C., Tolosana, R., Vera-Rodriguez, R., Lopes, V., Proenca, H., & Fierrez, J. (2020) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2020.3007250 · Cited by 12
Self-citation
Metadata
- Authors
- Galbally, J., Marcel, S., & Fierrez, J.
- Venue
- IEEE Access
- Published
- 2014
- Volume
- 2
- Pages
- 1530-1552
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2072273451
- Cited by
- 440
- References
- 112
- FWCI
- 27.15
- Type
- journal article
- Language
- en
- Open access
- gold
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: Biometric Antispoofing Methods: A Survey in Face Recognition. https://app.cochat.ai/papers/10.1109/access.2014.2381273
- The paper
- Galbally, J., Marcel, S., & Fierrez, J. (2014). Biometric Antispoofing Methods: A Survey in Face Recognition. IEEE Access. https://doi.org/10.1109/access.2014.2381273
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.
