Introduction to Face Presentation Attack Detection

Hernandez-Ortega, Javier; Fiérrez, Julián; Morales, Aythami; Galbally, Javier

doi:10.1007/978-3-319-92627-8_9

Cited by 42 publications

(29 citation statements)

References 35 publications

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“…The different approaches are then hierarchically classified into subclasses of these two broad categories. Hernandez-Ortega et al [ 40 ] divided the PAD methods as static or dynamic methods, depending on whether they take into account temporal information. Recently, Bhattacharjee et al [ 41 ] considered the PAD methods as approaches based on visible light and extended-range imagery.…”

Section: Overview Of Facial Pad Methods Using Only Rgb Cameras From Gcdsmentioning

confidence: 99%

A Survey on Anti-Spoofing Methods for Facial Recognition with RGB Cameras of Generic Consumer Devices

et al. 2020

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The widespread deployment of facial recognition-based biometric systems has made facial presentation attack detection (face anti-spoofing) an increasingly critical issue. This survey thoroughly investigates facial Presentation Attack Detection (PAD) methods that only require RGB cameras of generic consumer devices over the past two decades. We present an attack scenario-oriented typology of the existing facial PAD methods, and we provide a review of over 50 of the most influenced facial PAD methods over the past two decades till today and their related issues. We adopt a comprehensive presentation of the reviewed facial PAD methods following the proposed typology and in chronological order. By doing so, we depict the main challenges, evolutions and current trends in the field of facial PAD and provide insights on its future research. From an experimental point of view, this survey paper provides a summarized overview of the available public databases and an extensive comparison of the results reported in PAD-reviewed papers.

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Section: Overview Of Facial Pad Methods Using Only Rgb Cameras From Gcdsmentioning

confidence: 99%

A Survey on Anti-Spoofing Methods for Facial Recognition with RGB Cameras of Generic Consumer Devices

et al. 2020

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“…The exact set of attributes that can be included under the biometric bias term are still an open discussion issue as concluded in the EAB demographic fairness in biometric systems workshop [55]. The phenomena of bias in face biometrics were found in several disciplines such as presentation attack detection [19], [33], the estimation of facial characteristics [70], [15], and the assessment of face image quality [71]. In some previous works, factors that affect the recognition performance were also known as covariates [45], [44].…”

Section: Related Workmentioning

confidence: 99%

A Comprehensive Study on Face Recognition Biases Beyond Demographics

Terhörst

Kolf

Huber

et al. 2022

IEEE Trans. Technol. Soc.

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Face recognition (FR) systems have a growing effect on critical decision-making processes. Recent works have shown that FR solutions show strong performance differences based on the user's demographics. However, to enable a trustworthy FR technology, it is essential to know the influence of an extended range of facial attributes on FR beyond demographics. Therefore, in this work, we analyse FR bias over a wide range of attributes. We investigate the influence of 47 attributes on the verification performance of two popular FR models. The experiments were performed on the publicly available MAAD-Face attribute database with over 120M high-quality attribute annotations. To prevent misleading statements about biased performances, we introduced control group based validity values to decide if unbalanced test data causes the performance differences. The results demonstrate that also many non-demographic attributes strongly affect the recognition performance, such as accessories, hair-styles and colors, face shapes, or facial anomalies. The observations of this work show the strong need for further advances in making FR system more robust, explainable, and fair. Moreover, our findings might help to a better understanding of how FR networks work, to enhance the robustness of these networks, and to develop more generalized bias-mitigating face recognition solutions.

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“…A photo attack is a presentation attack in which the attacker displays a photograph of the victim to the sensor of the FR system. This photograph can be printed on paper or displayed on a device's screen (e.g., a smartphone, a tablet, or a laptop) [31]. A replay attack is another presentation attack in which a victim's video is played instead of a photograph [31].…”

Section: Face Recognitionmentioning

confidence: 99%

“…This photograph can be printed on paper or displayed on a device's screen (e.g., a smartphone, a tablet, or a laptop) [31]. A replay attack is another presentation attack in which a victim's video is played instead of a photograph [31]. A presentation attack detector can be integrated into an FR system to mitigate presentation attacks [31].…”

Section: Face Recognitionmentioning

confidence: 99%

Generating Master Faces for Use in Performing Wolf Attacks on Face Recognition Systems

Nguyen

Yamagishi

Echizen

et al. 2020

2020 IEEE International Joint Conference on Biometrics (IJCB)

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Face authentication is now widely used, especially on mobile devices, rather than authentication using a personal identification number or an unlock pattern, due to its convenience. It has thus become a tempting target for attackers using a presentation attack. Traditional presentation attacks use facial images or videos of the victim. Previous work has proven the existence of master faces, i.e., faces that match multiple enrolled templates in face recognition systems, and their existence extends the ability of presentation attacks. In this paper, we perform an extensive study on latent variable evolution (LVE), a method commonly used to generate master faces. We run an LVE algorithm for various scenarios and with more than one database and/or face recognition system to study the properties of the master faces and to understand in which conditions strong master faces could be generated. Moreover, through analysis, we hypothesize that master faces come from some dense areas in the embedding spaces of the face recognition systems. Last but not least, simulated presentation attacks using generated master faces generally preserve the false-matching ability of their original digital forms, thus demonstrating that the existence of master faces poses an actual threat.

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Introduction to Face Presentation Attack Detection

Cited by 42 publications

References 35 publications

A Survey on Anti-Spoofing Methods for Facial Recognition with RGB Cameras of Generic Consumer Devices

A Survey on Anti-Spoofing Methods for Facial Recognition with RGB Cameras of Generic Consumer Devices

A Comprehensive Study on Face Recognition Biases Beyond Demographics

Generating Master Faces for Use in Performing Wolf Attacks on Face Recognition Systems

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