bona fide presentation classification error rate

ISO/IEC 30107-3:2017 establishes:

- principles and methods for performance assessment of presentation attack detection mechanisms;

- reporting of testing results from evaluations of presentation attack detection mechanisms;

- a classification of known attack types (in an informative annex).

Outside the scope are:

- standardization of specific PAD mechanisms;

- detailed information about countermeasures (i.e. anti-spoofing techniques), algorithms, or sensors;

- overall system-level security or vulnerability assessment.

The attacks considered in this document take place at the sensor during presentation. Any other attacks are considered outside the scope of this document.

General information

• Status  :  Withdrawn Publication date  :  2017-09 Stage : Withdrawal of International Standard [ 95.99 ]
• Edition  : 1 Number of pages  : 33
• Technical Committee : ISO/IEC JTC 1/SC 37 ICS  : 35.240.15  
• RSS  updates
• ISO/IEC 30107-3:2017
• 00 Preliminary
• 10.99 2014-08-19 New project approved
• 20 Preparatory
• 30.00 2015-08-04 Committee draft (CD) registered
• 30.20 2015-09-16 CD consultation initiated
• 30.60 2015-11-18 Close of comment period
• 30.99 2016-03-02 CD approved for registration as DIS
• 40.00 2016-08-15 DIS registered
• 40.20 2016-10-13 DIS ballot initiated: 12 weeks
• 40.60 2017-01-06 Close of voting
• 40.99 2017-03-06 Full report circulated: DIS approved for registration as FDIS
• 50.00 2017-04-06 Final text received or FDIS registered for formal approval
• 50.20 2017-05-31 Proof sent to secretariat or FDIS ballot initiated: 8 weeks
• 50.60 2017-07-28 Close of voting. Proof returned by secretariat
• 60.00 2017-07-28 International Standard under publication
• 60.60 2017-08-23 International Standard published
• 90.92 2019-09-19 International Standard to be revised
• 95.99 2023-01-10 Withdrawal of International Standard

ISO/IEC 30107-3:2023

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Liveness Detection Certification, FIDO Certification and PAD Evaluations

Process & standards for certified liveness detection.

The following sections are geared at enabling corporates in need for a PAD solution to estimate the informative value of a liveness detection certification.

After reading this article you will know:

• The context and history of liveness detection certifications
• The process for receiving a certified liveness detection
• Different levels of presentation attacks
• How biometric anti-spoofing performance is measured
• The limitations of liveness detection certification

ISO Compliant Presentation Attack Detection

The knowledge for this article is based on BioID’s experience with two different types of liveness detection certifications: BioID has performed a PAD evaluation with the German testing laboratory TÜV Informationstechnik GmbH (TÜViT). The audit confirms the ISO 30107-3 compliance of BioID’s presentation attack detection technology. The FIDO accredited biometric laboratory TÜViT tested against criteria based on FIDO Biometric Certification Requirements v1.1 (FIDO1.1) and ISO/IEC 30107-3:2017 (ISO/IEC30107-3). The applied criteria are considered by TÜViT to be stricter than FIDO1.1, so as the test was successful, the FIDO criteria are also regarded as being met. In addition, ARIADNEXT, a long-term BioID customer, offers an identity verification solution which has been certified with a FIDO biometric component certification in November 2020. This solution includes BioID’s liveness detection for fraud prevention. It’s reliability was thoroughly tested during the FIDO testing procedures in 2020.

The ISO standard ISO/IEC 30107-3 for presentation attack detection was introduced in September 2017. This norm has been incorporated in the official FIDO certification process. Also, it has led to independent laboratories offering PAD evaluations based on this ISO norm. If a certified liveness detection is based on this ISO standard ISO/IEC 30107-3, there is a certain element of comparability between the different evaluations. Still, it is important to take a closer look at the testing protocols and results as various laboratories or test centers differ in how they carry out the performance assessment. This can result in disparity of the informative value of liveness detection certifications . Due to limited test scenarios, results might not be transferable to real-life scenarios. Hence, it may always be recommended to perform individual performance assessment before deciding for an anti-spoofing solution .

How Liveness Detection Certification Works

A biometric vendor trying to offer a certified liveness detection needs to engage with one of the accredited laboratories and provide a software/hardware solution for evaluation. The small number of accredited laboratories to perform a liveness detection certification include e.g. the German TÜV IT, the Swiss Center for Biometrics Research and Testing (Idiap Research Institute) and the French ELITT/Leti CEA. A full list of accredited test centers can be found in the resources list below.

For offering a certified liveness detection, biometric vendors can either choose to perform a FIDO certification or do a mere PAD evaluation. In addition to the PAD performance, a FIDO certification also includes the biometric verification (facial recognition) performance. During both performance tests, biometric anti-spoofing systems are challenged with different presentation attacks such as printed photos, paper masks or videos. If successfully tested according to ISO/IEC 30107-3, the biometric vendor receives a testing report (in the case of a PAD evaluation) or a FIDO certificate (if a full FIDO certification is performed).

Which Presentation Attacks are Tested

According to FIDO Biometrics Requirements published in 06/2019, there are three levels of presentation attack scenarios which can be tested against. These differ mainly in the time, expertise and equipment needed to create the attack (see table 1). Level A includes simple photo printouts, or a photo presented on a smartphone display, whereas level B additionally includes paper masks or videos of a person. Level C represents silicon masks, as well as high-quality videos of a person presented on a high-resolution display, for instance. Each of these attack examples is called a presentation attack instrument (PAI) . The testing is made with classes of attacks, the so called PAI species. Such a PAI species can be an iPhone 8 display presenting photos of various people. Another example of a PAI species are photos of different people printed on the same paper with the same printer. PAD evaluations can be made for each of the levels A-C separately. For a FIDO certification, an algorithm needs to detect spoofing attempts on levels A and B by default.

How Certified Liveness Performance is Measured

For a certified liveness detection, certain values are calculated to assess the performance: APCER (Attack Presentation Classification Error Rate) and BPCER (Bona-fide Presentation Classification Error Rate). APCER calculates the proportion of attacks mistakenly being classified as live persons. BPCER represents the proportion of live persons (also called Bona-fide) being classified as fakes. For mere PAD evaluation, no verification performance takes place. It is only tested whether a presented face came from a live person or not. For a full FIDO certification, the value calculated also includes the verification (facial recognition) result, resulting in IAPMR (Impostor Attack Presentation Match Rate). This number describes the proportion of attacks by impostors incorrectly accepted as the real person.

For receiving a certified liveness detection included in a FIDO certification, 10 presentation attack species (6 from level A and 4 from level B, see table 1) as well as 10 people (subjects) are needed. This results in 100 PAI. Each has a maximum of 5 attempts to spoof the liveness detection. In other words, 50 attempts per species, or 500 spoofing attempts are used altogether. In order to successfully complete this part of the performance testing, the IAPMR needs to be less than 20 % per PAI species, meaning less than 10 of the 50 attempts per species may wrongly be classified as a live person. For more details please see FIDO’s PAD criteria . In order to provide the desired objective performance measurement, liveness detection certifications that are not part of a FIDO certification process should be designed similarly.

Limitations of Liveness Detection Certifications

To be precise, there is no certification for presentation attack detection available on the market, as the international standard for PAD, ISO/IEC 30107-3, does not offer a standardized testing protocol which could be used to perform comparable testing and certification. As a consequence, there is no certified liveness detection solution available on the market, either. Instead, biometric testing laboratories like TÜViT from Germany or Idiap from Switzerland can perform evaluations based on the ISO standard and thus confirm a technology’s ISO compliance. Still, the term ‘certification’ or ‘certified liveness detection’ has been widely used. This has sometimes happened in a misleading way as the following article by a leading Professor at Clarkson University and at Biometrics Institute describes: Go to the Biometric Update article .

There are certain limitations of liveness detection certifications, which should be kept in mind when using them as decision criterion. For instance, for a PAD evaluation that is independent from FIDO, the vendor himself can choose and decide the level of PAI, the number of attacks and the number of subjects. Even for a strongly regulated FIDO certification, the number of devices and spoofing scenarios tested is limited. This should be kept in mind as the biometric anti-spoofing can perform differently on unseen presentation attacks. Also, when reviewing the test reports of evaluated/certified solutions, there are details which should be looked at closely, for instance, the false rejection of live people (BPCER). This calculation is often kept unmentioned as there is a direct link between APCER and BPCER: if the solution is designed to perform extremely well on APCER (correctly rejecting fakes), this can result in a higher BPCER (falsely rejecting live people). In a real-world scenario, of course, this has to be balanced in order to create security and usability at the same time.

Compliant Presentation Attack Detection

As a German biometrics company with more than 20 years of experience in the market, BioID sees with great interest how the biometrics industry is changing. With its multiple patents, BioID is a leading player worldwide to offer software-based biometric anti-spoofing. As the demand for facial recognition is growing, the market has seen many new entrants and a broad diversification of offerings. As such, standards like the ISO/IEC 30107-3 offer guidelines for an objective measurement for presentation attack detection performance. For a buying decision, while a liveness detection certification does facilitate the assessment process, one must pay attention to their intended application by considering factors such as ease of integration, user experience, as well as vendor credibility (e.g. in terms of GDPR). Every application scenario is different and the best way to find a suitable anti-spoofing solution is to find a trusted & experienced vendor.

BioID Liveness Detection can be tested and evaluated at the BioID Playground . For integration of our Web-APIs into your services, please request a free trial instance . Contact us for more information.

Resources on Liveness Detection Certification

• For more information on FIDO Biometric Component Testing please see: https://fidoalliance.org/certification/biometric-component-certification/
• FIDO Biometrics Requirements; Final Document, June 06, 2019: https://fidoalliance.org/specs/biometric/requirements/
• ISO/IEC 30107-3:2017 Information technology — Biometric presentation attack detection — Part 3: Testing and reporting: https://www.iso.org/standard/67381.html
• Link to accredited test laboratories: https://fidoalliance.org/certification/biometric-component-certification/fido-accredited-biometric-laboratories/

Ann-Kathrin Freiberg +49 911 9999 898 0 [email protected]

Standards for Biometric Presentation Attack Detection

• First Online: 02 January 2019

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• Christoph Busch 6  

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

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This chapter reports about the relevant international standardization activities in the field of biometrics and specifically describes standards on presentation attack detection that have established a framework including a harmonized taxonomy for terms in the field of liveness detection and spoofing attack detection, an interchange format for data records and moreover a testing methodology for presentation attack detection. The scope and of the presentation attack detection multipart standard ISO/IEC 30107 is presented. Moreover, standards regarding criteria and methodology for security evaluation of biometric systems are discussed.

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Busch, C. (2019). Standards for Biometric Presentation Attack Detection. In: Marcel, S., Nixon, M., Fierrez, J., Evans, N. (eds) Handbook of Biometric Anti-Spoofing. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-319-92627-8_22

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Complete playbook to understand liveness detection industry

What is APCER (Attack Presentation Classification Error Rate)?

On June 6th, 2024, the FATE Morph research publication by NIST entitled NISTIR 8292 DRAFT SUPPLEMENT was updated. This testing is regularly carried out on facial recognition solutions to analyze their performance in presentation attack detection like face morphing . Two primary quantities; APCER & BPCER are reported that are the determinants of the accuracy and robustness of a facial recognition tool.

In this knowledge base, we will explain the first quantity metric i.e. APCER.

What is APCER?

APCER is an acronym for Attack Presentation Classification Error Rate which is defined as the frequency of a biometric facial recognition tool with which it wrongly accepts a biometric presentation attack as a genuine face presentation. It is also termed the ‘Morph Miss Rate’ in NIST research and testing publications.

How is APCER Calculated?

It is calculated in the following equation:

The number of morph images wrongly classified as bona fide images is divided by the total number of actual morph attack images.

APCER vs. BPCER

As per NIST findings, it is not possible to calculate APCER alone until and unless:

• A Presentation Attack Image curation is completed
• BPCER values are set, fixed & kept below such as:
• APCER @ BPCER = 0.01 in Fate Morph Detection (For Face Morphing and Impersonation attacks)
• APCER @ BPCER = 0.0001 in FRVT PAD API (for all other Presentation attack types)

What is the Ideal APCER?

Based on the above set value, APCER  values are calculated for different biometric identity solution providers by NIST. 

Look at the statistical explanation of APCER’s calculation above a threshold below:

Here is the analytical summary of APCER in biometrics values under different image sets presented to the algorithm tested by NIST :

Single-Image Morphing Detection

A single image (morphed or bona fide) is presented to the algorithm. 

Here are the summarized results:

From the above findings, it is clear that few biometric system algorithms have achieved 0.000 APCER under specific conditions. However, the dataset contains 22 algorithmic submissions under each data set and tier. This shows that solutions need to improve their accuracy levels to minimize presentation attack acceptance rate.

A further summary of test results related to APCER vs. BPCER is given in the knowledge piece of BPCER .

Impact of APCER Value in Biometric Facial Recognition

It is evident that if APCER in biometrics is high there is a high chance of identity theft attempts going undetected resulting in bigger threats like:

• Account Takeover fraud
• Financial scams
• Crypto fraud and theft
• Money Laundering

It is important to keep the face presentation attack detection in place by using an AI-powered facial recognition solution that is compliant with NIST standards in achieving the lowest possible error rates. For this purpose, the facial recognition tool must be regularly tested on NIST metrics with improvements in algorithms to reach a perfect zero. In this way, the solution will become spoof-proof for Biometric PAD (Presentation Attack Detection) using facial recognition. Moreover, the solutions need to improve their liveness detection feature to ensure the prevention of presentation attacks & their error rates well before time.

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