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  • Perspective
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Challenges for automated face recognition systems

Nature Reviews Electrical Engineering volume 1, pages 748–757 (2024)Cite this article

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Abstract

Face recognition, as a process of the human visual system, analyses facial properties and contextual information such as body shape. Automated recognition replicates the human process and analyses a face image, which is typically acquired with a visible spectrum sensor. When dealing with automated operational systems, the quality of the captured face image is relevant as it affects the recognition accuracy. Thus, it is necessary to measure the utility of a face sample with both a quality score and complementary measures that can provide actionable feedback. This Perspective addresses challenges and discusses solutions for the optimization of biometric recognition systems specifically related to face image analysis. One of these challenges is the vulnerability to presentation attacks. Consequently, for reliable recognition in non-supervised environments, robust presentation attack detection is required. Moreover, biometric templates must be protected. Finally, acceptability of biometric systems requires fairness of the biometric algorithms and artificial neural networks used.

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Fig. 1: Example of an error-versus-discard-characteristic curve.
Fig. 2: Potential points of attack.
Fig. 3: Example of morphing attack.
Fig. 4: Differential morphing attack detection.

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Acknowledgements

This research work has been funded by the German Federal Ministry of Education and Research and the Hessian Ministry of Higher Education, Research, Science and the Arts, within their joint support of the National Research Center for Applied Cybersecurity ATHENE.

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  1. Norwegian Biometrics Laboratory (NBL), Norwegian University of Science and Technology (NTNU), Gjøvik, Norway

    Christoph Busch

  2. Computer Science Department, Hochschule Darmstadt (HDA), Darmstadt, Germany

    Christoph Busch

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Correspondence to Christoph Busch.

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Related links

BOEP platform: https://biolab.csr.unibo.it/fvcongoing/UI/Form/BOEP.aspx

Coded Bias: https://en.wikipedia.org/wiki/Coded_Bias

Open Source Face Image Quality framework: https://bsi.bund.de/dok/OFIQ-e

Glossary

Artefacts

Artificial objects or representation presenting a copy of biometric characteristics or synthetic biometric patterns.

Biometric applications

Applications that include the policies that govern the operation of the biometric system.

Biometric attendant

Agent of the biometric system operator who directly interacts with the biometric capture subject.

Biometric capture

Obtaining and recording of, in a retrievable form, signal(s) of biometric characteristic(s) directly from individual(s), or from representation(s) of biometric characteristic(s).

Biometric capture device

Device that collects a signal from a biometric characteristic and converts it to a captured biometric sample.

Biometric capture process

Series of actions undertaken to effect a biometric capture.

Biometric capture subjects

Individuals who are the subject of a biometric capture process.

Biometric characteristics

Biological and/or behavioural characteristics of an individual from which distinguishable, repeatable biometric features can be extracted for the purpose of biometric recognition.

Biometric concealer

Subversive biometric capture subject who performs a biometric concealment attack.

Biometric features

Numbers or labels extracted from biometric samples and used for comparison.

Biometric impostor

Subversive biometric capture subject who performs a biometric impostor attack.

Biometric probe

Biometric sample or biometric feature set input to an algorithm for comparison to a biometric reference(s).

Biometric recognition

Automated recognition of individuals based on their biological and behavioural characteristics.

Biometric reference

One or more stored biometric samples, biometric templates or biometric models attributed to a biometric data subject and used as the object of biometric comparison.

Biometric samples

Analog or digital representations of biometric characteristics prior to biometric feature extraction.

Biometric utility

Degree to which a biometric sample supports biometric recognition performance.

Bona fide presentation

Biometric presentation without the goal of interfering with the operation of the biometric system.

Canonical face image

Face image conformant to an external standard or specification of a reference face image.

Comparison score

Numerical value (or set of values) resulting from a comparison.

Error-versus-discard-characteristic curve

Method to evaluate the efficacy of quality assessment algorithms by quantifying how efficiently discarding samples with low quality scores results in improved (reduced) false non-match rate.

False match rate

(FMR). Proportion of the completed biometric non-mated comparison trials that result in a false match.

False non-match rate

(FNMR). Proportion of the completed biometric mated comparison trials that result in a false non-match.

Morph image

Face image representing two or more parent facial images.

Morphing attack

Biometric image manipulation attack through merging two or more facial images by means of morphing.

Morphing attack detection

(MAD). Detecting traces of a face image morphing attack conducted by some algorithms and/or human examiner.

Presentation attack

Presentation to the biometric capture subsystem with the goal of interfering with the operation of the biometric system.

Presentation attack detection

(PAD). Automated discrimination between bona fide presentations and biometric presentation attacks.

Presentation attack instrument

Biometric characteristic or object used in a biometric presentation attack.

Quality components

Measurements on the biometric sample that may contribute to the computation of a unified quality score.

Quality measures

Quality scores or quality components.

Quality score

Quantitative value of the fitness of a biometric sample to accomplish or fulfil the comparison decision.

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Busch, C. Challenges for automated face recognition systems. Nat Rev Electr Eng 1, 748–757 (2024). https://doi.org/10.1038/s44287-024-00094-x

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