Biometric Antispoofing Methods: A Survey in Face Recognition

By Javier Galbally, Sébastien Marcel, Julian Fierrez. IEEE Access, 2014

418 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/bef926d63512dbffcf1af59f72295ef497f5acf9

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Citations in the corpus

DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection (2020, background): “These renewed efforts in fake face detection are built around past research in biometric anti-spooﬁng [8]–[10] and modern data-driven deep learning [11].”
Secure Face Unlock: Spoof Detection on Smartphones (2016, background): “As summarized in [39], studies on face spoofing detection date back over 15 years.”
On the vulnerability of extended Multispectral face recognition systems towards presentation attacks (2017, methodology): “Thus, it is naturally assumed that the multispectral sensors are resistant to different kinds of PAIs.”
Presentation Attack Detection Methods for Face Recognition Systems (2017, background): “The third type of static approach includes hybrid schemes that combine more than one attribute [Galbally et al. 2014b; Wen et al. 2015], the use of client identity information [Chingovska and Anjos 2015], the characterization of the defocus property of the captured face image [Kim et al. 2015b;…”
OULU-NPU: A Mobile Face Presentation Attack Database with Real-World Variations (2017, background): “The vulnerabilities of face-based biometric systems to spoofing have been now recognized and face presentation attack detection (PAD) has finally received significant attention in the research community [1], [3], [7].”
GANprintR: Improved Fakes and Evaluation of the State of the Art in Face Manipulation Detection (2019, neutral): “) [3], [4].”
Deep Models and Shortwave Infrared Information to Detect Face Presentation Attacks (2020, background): “attack detection (PAD) approaches have been proposed in the last decade, and surveys can be found in [6] and [7].”