Biometrics: Trust, but Verify
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Abstract (arXiv)
Over the past two decades, biometric recognition has exploded into a plethora of different applications around the globe. This proliferation can be attributed to the high levels of authentication accuracy and user convenience that biometric recognition systems afford end-users. However, in-spite of the success of biometric recognition systems, there are a number of outstanding problems and concerns pertaining to the various sub-modules of biometric recognition systems that create an element of mistrust in their use - both by the scientific community and also the public at large. Some of these problems include: i) questions related to system recognition performance, ii) security (spoof attacks, adversarial attacks, template reconstruction attacks and demographic information leakage), iii) uncertainty over the bias and fairness of the systems to all users, iv) explainability of the seemingly black-box decisions made by most recognition systems, and v) concerns over data centralization and user privacy. In this paper, we provide an overview of each of the aforementioned open-ended challenges. We survey work that has been conducted to address each of these concerns and highlight the issues requiring further attention. Finally, we provide insights into how the biometric community can address core biometric recognition systems design issues to better instill trust, fairness, and security for all.
Key facts. Cited by 39 papers in the map (data through 14 Oct 2026). Builds on 118 of its 276 references that are in the map. First posted on arXiv 14 May 2021.
Builds on
118 of 276 references.
References · 118
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50 most cited of 118 All 118 (JSON)
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Citing papers per year: 2021: 3; 2022: 13; 2023: 9; 2024: 7; 2025: 6; 2026: 1.
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- 2023L-DAWA: Layer-wise Divergence Aware Weight Aggregation in Federated Self-Supervised Visual Representation Learning14 Jul 2023
- 2023Efficient Explainable Face Verification based on Similarity Score Argument Backpropagation26 Apr 2023
- 2023Fitness-for-Duty Classification using Temporal Sequences of Iris Periocular images24 Apr 2023
- 2023Joint Person Identity, Gender and Age Estimation from Hand Images using Deep Multi-Task Representation Learning27 Mar 2023
- 2023Introduction to Presentation Attack Detection in Fingerprint Biometrics14 Feb 2023
- 20233D Face Reconstruction for Forensic Recognition -- A Survey3 Feb 2023
- 2023M3FAS: An Accurate and Robust MultiModal Mobile Face Anti-Spoofing System30 Jan 2023
- 2022Deep representation learning: Fundamentals, Perspectives, Applications, and Open Challenges27 Nov 2022
- 2022edBB-Demo: Biometrics and Behavior Analysis for Online Educational Platforms16 Nov 2022
- 2022Haven't I Seen You Before? Assessing Identity Leakage in Synthetic Irises3 Nov 2022
- 2022Deep Learning for Iris Recognition: A Survey12 Oct 2022
- 2022Local-Aware Global Attention Network for Person Re-Identification Based on Body and Hand Images11 Sep 2022
- 2022Learning to Predict Fitness for Duty using Near Infrared Periocular Iris Images4 Sep 2022
- 2022The Value of AI Guidance in Human Examination of Synthetically-Generated Faces22 Aug 2022
- 2022Synthetic Data in Human Analysis: A Survey19 Aug 2022
- 2022Federated Self-supervised Learning for Video Understanding5 Jul 2022
- 2022A Comprehensive Survey on Deep Gait Recognition: Algorithms, Datasets and Challenges28 Jun 2022
- 2022An Overview of Privacy-enhancing Technologies in Biometric Recognition21 Jun 2022
- 2022On Demographic Bias in Fingerprint Recognition19 May 2022
- 2022Dictionary Attacks on Speaker Verification24 Apr 2022
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- 2023L-DAWA: Layer-wise Divergence Aware Weight Aggregation in Federated Self-Supervised Visual Representation Learningcited by 30
- 2022Synthetic Data in Human Analysis: A Surveycited by 28
- 2022Deep Learning for Iris Recognition: A Surveycited by 25
- 2022edBB-Demo: Biometrics and Behavior Analysis for Online Educational Platformscited by 18
- 2022An Overview of Privacy-enhancing Technologies in Biometric Recognitioncited by 17
- 2022Federated Self-supervised Learning for Video Understandingcited by 13
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- 2022The Value of AI Guidance in Human Examination of Synthetically-Generated Facescited by 8
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- 2025Mitigating Bias with Words: Inducing Demographic Ambiguity in Face Recognition Templates by Text Encoding5 Dec 2025
- 2025Deceptive Beauty: Evaluating the Impact of Beauty Filters on Deepfake and Morphing Attack Detection17 Sep 2025
- 2025An Investigation of Ear-EEG Signals for a Novel Biometric Authentication System17 Jul 2025
- 2025ColFigPhotoAttnNet: Reliable Finger Photo Presentation Attack Detection Leveraging Window-Attention on Color Spaces7 Mar 2025
- 2025Review of Demographic Fairness in Face Recognition4 Feb 2025
- 2025Deep Learning in Palmprint Recognition-A Comprehensive Survey2 Jan 2025
- 2024LabellessFace: Fair Metric Learning for Face Recognition without Attribute Labels14 Sep 2024
- 2024Privacy-Safe Iris Presentation Attack Detection5 Aug 2024
- 2024Open-Set Biometrics: Beyond Good Closed-Set Models23 Jul 2024
- 2024Beyond Spatial Explanations: Explainable Face Recognition in the Frequency Domain16 Jul 2024
- 2024Can the accuracy bias by facial hairstyle be reduced through balancing the training data?30 May 2024
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