Challenges for automated face recognition systems (2024)

References

  1. International Organization for Standardization. ISO/IEC 2382-37:2022. Information Technology — Vocabulary — Part 37: Biometrics (ISO, 2022).

  2. Drozdowski, P., Rathgeb, C. & Busch, C. Computational workload in biometric identification systems: an overview. IET Biom. 8, 351–368 (2019).

    Article Google Scholar

  3. International Organization for Standardization. ISO/IEC 19795-1:2021. Information Technology — Biometric Performance Testing and Reporting — Part 1: Principles and Framework (ISO, 2021).

  4. Meints, M. et al. Biometric systems and data protection legislation in Germany. In Proc. 2008 International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIHMSP), 1088–1093 (IEEE, 2008).

  5. Funk, W., Arnold, M., Busch, C. & Munde, A. Evaluation of image compression algorithms for fingerprint and face recognition. In Proc. IEEE Information Assurance Workshop (IEEE, 2005).

  6. International Organization for Standardization. ISO/IEC 39794-5:2019 Information Technology — Extensible Biometric Data Interchange Formats — Part 5: Face image data (ISO, 2019).

  7. International Organization for Standardization. ISO/IEC FDIS 29794-5 Information Technology Biometric Sample Quality. Part 5: Face image data (ISO, 2024).

  8. Tabassi, E. & Wilson, C. A novel approach to fingerprint image quality. In 2005 International Conference on Image Processing (ICIP 2005), 37–40 (IEEE, 2005).

  9. Olsen, M., Sˇmida, V. & Busch, C. Finger image quality assessment features — definitions and evaluation. IET Biom. 5, 47–64 (2016).

    Article Google Scholar

  10. Tabassi, E. et al. NIST Interagency Report 8382 (National Institute of Standards and Technology, 2021).

  11. European Council. Regulation 2017/2226 of the European Parliament and of the Council of 30 November 2017 on establishing an Entry/Exit System (EES) to register entry and exit data and refusal of entry data of third-country nationals (European Council, 2017).

  12. European Council. Commission Implementing Decision 2019/329 of 25 February 2019 laying down the specifications for the quality, resolution and use of fingerprints and facial image for biometric verification and identification in the Entry/Exit System (EES) (European Council, 2019).

  13. Schlett, T. et al. Face image quality assessment: a literature survey. ACM Computing Surveys (CSUR). 54, (2022).

  14. International Civil Aviation Organization. NTWG: Machine Readable Travel Documents Part 3 — Specifications for Electronically Enabled MRtds with Biometric Identification Capability (ICAO, 2021).

  15. International Organization for Standardization. ISO/IEC 19794-5:2011 Information Technology — Biometric Data Interchange Formats — Part 5: Face Image Data (ISO, 2011).

  16. ICAO. Machine Readable Travel Documents. https://www.icao.int/publications/documents/9303_p9_cons_en.pdf (2021).

  17. International Organization for Standardization. ISO/IEC 29794-1 Information Technology — Biometric Sample Quality — Part 1: Framework (ISO, 2024).

  18. Meng, Q., Zhao, S., Huang, Z. & Zhou, F. MagFace: a universal representation for face recognition and quality assessment. In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, 2021).

  19. Boutros, F., Fang, M., Klemt, M., Fu, B. & Damer, N. CR-FIQA: face image quality assessment by learning sample relative classifiability. In Conference on Computer Vision and Pattern Recognition (CVPR), 5836–5845 (IEEE, 2023).

  20. Deng, J., Guo, J. & Zafeiriou, S. ArcFace: additive angular margin loss for deep face recognition. In Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, 2019).

  21. Huang, G. B., Ramesh, M., Berg, T. & Learned-Miller, E. Labeled faces in the wild: a database for studying face recognition in unconstrained environments. https://api.semanticscholar.org/CorpusID:88166 (2008).

  22. Schlett, T., Rathgeb, C., Tapia, J. & Busch, C. Considerations on the evaluation of biometric quality assessment algorithms. IEEE Trans Biometrics Behav. Identity Sci. https://doi.org/10.1109/TBIOM.2023.3336513 (2023).

  23. Chandaliya, P., Raja, K., Raghavendra, R. & Busch, C. Unified face image quality score based on ISO/IEC quality components. In Proc. International Conference of the Biometrics Special Interest Group (BIOSIG), 1–11 (2023).

  24. Grimmer, M., Rathgeb, C., Veldhuis, R. & Busch, C. Neutrex: a 3D quality component measure on facial expression neutrality. In Proc. International Joint Conference on Biometrics (IJCB), 1–8 (IEEE, 2023).

  25. Grimmer, M., Veldhuis, R. & Busch, C. Efficient expression neutrality estimation with application to face recognition utility prediction. In Proc. International Workshop on Biometrics and Forensics 1–8 (IWBF, 2024).

  26. Funk, W., Arnold, M., Busch, C. & Munde, A. Evaluation of image compression algorithms for fingerprint and face recognition systems. In Proc. IEEE SMC Information Assurance Workshop 72–78 (2005).

  27. Schlett, T., Schachner, S., Rathgeb, C., Tapia, J. & Busch, C. Effect of lossy compression algorithms on face image quality and recognition. In Intl Conference on Acoustics, Speech, and Signal Processing (ICASSP) (IEEE, 2023).

  28. Raghavendra, R. & Busch, C. Presentation attack detection methods for face recognition systems: a comprehensive survey. ACM Comput. Surv. 50, 1–37 (2017).

    Google Scholar

  29. Zwiesele, A., Munde, A., Busch, C. & Daum, H. BioIS study — comparative study of biometric identification systems. In 34th Annual 2000 IEEE International Carnahan Conference on Security Technology (CCST) (IEEE, 2000).

  30. Matsumoto, T. et al. Impact of artificial ‘gummy’ fingers on fingerprint systems. SPIE Conf. Opt. Security Counterfeit Deterrence Tech. IV 4677, 275–289 (2002).

    Article Google Scholar

  31. Schuckers, S. et al. Issues for liveness detection in biometrics. In Proc. Biometric Consortium Conference. 6911 (NISTIR, 2002).

  32. International Organization for Standardization. ISO/IEC 30107-1. Information Technology — Biometric Presentation Attack Detection — Part 1: Framework (ISO, 2023).

  33. International Organization for Standardization. ISO/IEC 30107-2. Information Technology — Biometric Presentation Attack Detection — Part 2: Data Formats (ISO, 2017).

  34. International Organization for Standardization. ISO/IEC 30107-3. Information Technology — Biometric Presentation Attack Detection — Part 3: Testing and Reporting (ISO, 2023).

  35. International Organization for Standardization. ISO/IEC SC37 SD11. General Biometric System (ISO, 2008).

  36. Rathgeb, C., Drozdowski, P. & Busch, C. Detection of makeup presentation attacks based on deep face representations. In Proc. Intl Conference on Pattern Recognition (ICPR), 3443–3450 (2020).

  37. Rathgeb, C., Drozdowski, P. & Busch, C. Makeup presentation attacks: review and detection performance benchmark. IEEE Access. 8, 224958–224973 (2020).

    Article Google Scholar

  38. Rathgeb, C., Tolosana, R., Vera, R. & Busch, C. (eds) Handbook of Digital Face Manipulation and Detection: from DeepFakes to Morphing Attacks. 1st edn (Springer, 2022).

  39. Rathgeb, C., Dantcheva, A. & Busch, C. Impact and detection of facial beautification in face recognition: an overview. IEEE Access. 7, 152667–152678 (2019).

    Article Google Scholar

  40. Khodabakhsh, A., Raghavendra, R., Raja, K., Wasnik, P. & Busch, C. Fake face detection methods: can they be generalized? In 2018 International Conference of the Biometrics Special Interest Group. 1–6 (BIOSIG, 2018).

  41. Scherhag, U., Rathgeb, C., Merkle, J., Breithaupt, R. & Busch, C. Face recognition systems under morphing attacks: a survey. IEEE Access 7, 23012–23026 (2019).

  42. Ibsen, M. et al. Conditional face image manipulation detection: combining algorithm and human examiner decisions. In Proc. 12th Workshop on Information Hiding and Multimedia Security (ACM IH & MMSEC, 2024).

  43. Davis, J. et al. The super-recogniser advantage extends to the detection of digitally manipulated faces. Preprint at https://osf.io/preprints/psyarxiv/ye7ph (2024).

  44. Ferrara, M., Franco, A., Maltoni, D. & Busch, C. Morphing attack potential. In 10th International Workshop on Biometrics and Forensics (IWBF, 2022).

  45. FRONTEX Report. International Conference on Biometrics for Borders Morphing and Morphing Attack Detection Methods 2020, https://www.frontex.europa.eu/assets/Publications/Research/International_Conference_on_Biometrics_for_Borders.pdf (2024).

  46. Godage, S. et al. Analyzing human observer ability in morphing attack detection — where do we stand? IEEE Trans. Technol. Soc. 4, 125–145 (2023).

    Article Google Scholar

  47. Nichols, R., Rathgeb, C., Drozdowski, P. & Busch, C. Psychophysical evaluation of human performance in detecting digital face image manipulations. IEEE Access. 10, 31359–31376 (2022).

    Article Google Scholar

  48. Raja, K. et al. Morphing attack detection — database, evaluation platform and benchmarking. IEEE Trans Inf. Forensics Secur. 16, 4336–4351 (2020).

    Article Google Scholar

  49. Zhang, H. et al. MIPGAN — generating strong and high quality morphing attacks using identity prior driven GAN. IEEE Trans. Biometr. Behav. Identity Sci. 3, 365–383 (2021).

  50. Ngan, M., Grother, P., Hanaoka, K. & Kuo, J. Face Analysis Technology Evaluation (Fate) Part 4: Morph — Performance of Automated Face Morph Detection. NIST Interagency Report 8292 (National Institute of Standards and Technology, 2023).

  51. Raghavendra, R., Raja, K. & Busch, C. Detecting morphed face images. In 2016 IEEE 8th International Conference on Biometrics: Theory, Applications and Systems (BTAS) (IEEE, 2016).

  52. Scherhag, U., Kunze, J., Rathgeb, C. & Busch, C. Face morph detection for unknown morphing algorithms and image sources: a multi-scale block local binary pattern fusion approach. IET Biom. 9, 278–289 (2020).

    Article Google Scholar

  53. Scherhag, U., Budhrani, D., Gomez-Barrero, M. & Busch, C. Detecting morphed face images using facial landmarks. In Intl Conference on Image and Signal Processing (ICISP) (Springer, 2018).

  54. Debiasi, L., Scherhag, U., Rathgeb, C., Uhl, A. & Busch, C. PRNU-based detection of morphed face images. In 6th International Workshop on Biometrics and Forensics, 1–6 (2018).

  55. Scherhag, U., Debiasi, L., Rathgeb, C., Busch, C. & Uhl, A. Detection of face morphing attacks based on PRNU analysis. IEEE Trans. Biometr. Behav. Identity Sci. 1, 302–317 (2019).

  56. Raghavendra, R. et al. Transferable deep-CNN features for detecting digital and print-scanned morphed face images. In IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 1822–1830 (IEEE, 2017).

  57. Scherhag, U., Rathgeb, C., Merkle, J. & Busch, C. Deep face representations for differential morphing attack detection. IEEE Trans. Inf. Forensics Secur. 15, 3625–3639 (2020).

  58. Ngan, M., Grother, M. & Hanaoka, K. Face Recognition Vendor Test — Part 4A. NIST Interagency Report 8430 (National Institute of Standards and Technology, 2022).

  59. Scherhag, U. et al. Biometric systems under morphing attacks: assessment of morphing techniques and vulnerability reporting. In Intl Conference of the Biometric Special Interest Group BIOSIG 2017, 1–7 (IEEE, 2017).

  60. International Organization for Standardization. ISO/IEC DIS 20059. Information Technology Methodologies to Evaluate the Resistance of Biometric Recognition Systems to Morphing Attacks (ISO, 2024).

  61. Joshi, I. et al. Synthetic data in human analysis: a survey. IEEE Trans. Pattern Anal. Machine Intell. 46, 4957–4976 (2024).

    Article Google Scholar

  62. Goodfellow, I. J. et al. Generative adversarial nets. In Proc. 27th International Conference on Neural Information Processing Systems, Vol. 2, 2672–2680 (MIT Press, 2014).

  63. Karras, T. et al. Alias-free generative adversarial networks. In Proc. 35th Intl Conf. Neural Inform. Processing Syst. (NIPS ‘21) 66, 852–863 (Curran Assoc., 2024).

  64. Dhariwal, P. & Nichol, A. Diffusion models beat gans on image synthesis. Adv. Neur. Inf. Proc. Syst. 34, 8780–8794 (2021).

    Google Scholar

  65. Melzi, P. et al. Gandiffface: controllable generation of synthetic datasets for face recognition with realistic variations. In 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 3078–3087 (IEEE Computer Society, 2023).

  66. O’Sullivan, D. A high school student created a fake 2020 candidate. Twitter verified it. CNN https://edition.cnn.com/2020/02/28/tech/fake-twitter-candidate-2020/index (28 February 2020).

  67. Oltermann, P. European politicians duped into deepfake video calls with mayor of Kyiv. Guardian https://www.theguardian.com/world/2022/jun/25/european-leaders-deepfake-video-calls-mayor-of-kyiv-vitali-klitschko (25 June 2022).

  68. Ibsen, M., Rathgeb, C., Marcel, S. & Busch, C. Multi-channel cross modal detection of synthetic face images. In Proc. International Workshop on Biometrics and Forensics (IWBF), 1–8 (2024).

  69. Breebart, J., Busch, C., Grave, J. & Kindt, E. A reference architecture for biometric template protection based on pseudo identities. In BIOSIG 2008: Biometrics and Electronic Signatures, 25–37 (2008).

  70. International Organization for Standardization. ISO/IEC JTC1 SC27 Security Techniques: ISO/IEC 24745:2022. Information Technology — Security Techniques — Biometric Information Protection (ISO, 2022).

  71. Rathgeb, C., Breitinger, F. & Busch, C. Alignment-free cancelable iris biometric templates based on adaptive Bloom filters. In 2013 International Conference on Biometrics (ICB), 1–8 (2013).

  72. Gomez-Barrero, M., Rathgeb, C., Galbally, J., Fierrez, J. & Busch, C. Protected facial biometric templates based on local Gabor patterns and adaptive Bloom filters. In 2014 22nd International Conference on Pattern Recognition (ICPR), 4483–4488 (2014).

  73. Kolberg, J., Drozdowski, P., Gomez-Barrero, M., Rathgeb, C. & Busch, C. Efficiency analysis of post-quantum-secure face template protection schemes based on homomorphic encryption. In International Conference of the Biometrics Special Interest Group (BIOSIG) (2020).

  74. Guo, E. & Noori, H. This is the real story of the Afghan biometric databases abandoned to the Taliban. MIT Technol. Rev. https://www.technologyreview.com/2021/08/30/1033941/afghanistan-biometric-databases-us-military-40-data-points/ (30 August 2021).

  75. Drozdowski, P., Rathgeb, C., Dantcheva, A., Damer, N. & Busch, C. Demographic bias in biometrics: a survey on an emerging challenge. Trans. Technol. Soc. 1, 89–103 (2020).

    Article Google Scholar

  76. Cavazos, J. G. Phillips, P. J., Castillo, C. D. & O’Toole, A. J. Accuracy comparison across face recognition algorithms: where are we on measuring race bias? IEEE Trans. Biometr. Behav. Identity Sci. 3, 101–111 (2021).

    Article Google Scholar

  77. Bacchini, F. & Lorusso, L. Race, again: how face recognition technology reinforces racial discrimination. J. Inf. Commun. Ethics Soc. 17, 321–335 (2019).

    Article Google Scholar

  78. International Organization for Standardization. ISO/IEC FDIS 19795-10. Information Technology — Biometric Performance Testing and Reporting — Part 10: Quantifying Biometric System Performance Variation Across Demographic Groups (ISO, 2024).

  79. Grother, P., Ngan, M. & Hanaoka, K. NIST Interagency Report 8280. Face Recognition Vendor Test — Part 3 (National Institute of Standards and Technology, 2019).

  80. Fitzpatrick, T. The validity and practicality of sun-reactive skin types I through VI. Arch. Dermatol. 124, 869–871 (1988).

    Article Google Scholar

  81. Howard, J., Sirotin, Y., Tipton, J. & Vemury, A. Reliability and validity of image-based and self-reported skin phenotype metrics. IEEE Trans. Biometr. Behav. Identity Sci. 3, 550–560 (2021).

    Article Google Scholar

  82. Drozdowski, P., Rathgeb, C. & Busch, C. The watchlist imbalance effect in biometric face identification: comparing theoretical estimates and empiric measurements. In International Conference on Computer Vision Workshops (ICCVW), 1–9 (IEEE, 2021).

  83. Howard, J. et al. Evaluating proposed fairness models for face recognition algorithms. In Proc. International Conference on Pattern Recognition (IEEE, 2022).

  84. Rathgeb, C., Drozdowski, P., Frings, D. C., Damer, N. & Busch, C. Demographic fairness in biometric systems: what do the experts say? IEEE Technol. Soc. Mag. 41, 71–82 (2022).

    Article Google Scholar

  85. Kotwal, K. & Marcel, S. Fairness index measures to evaluate bias in biometric recognition. In Proc. International Conference on Pattern Recognition (IEEE, 2022).

  86. Terhörst, P., Kolf, J., Damer, N., Kirchbuchner, F. & Kuijper, A. Post-comparison mitigation of demographic bias in face recognition using fair score normalization. Pattern Recognit. Lett. 140, 332–338 (2020).

    Article Google Scholar

  87. Kolberg, J., Schäfer, Y., Rathgeb, C. & Busch, C. On the potential of algorithm fusion for demographic bias mitigation in face recognition. IET Biom. https://doi.org/10.1049/2024/1808587 (2023).

  88. Terhörst, P., Kolf, J., Damer, N., Kirchbuchner, F. & Kuijper, A. Face quality estimation and its correlation to demographic and non-demographic bias in face recognition. In 2020 IEEE International Joint Conference on Biometrics (IJCB), https://doi.org/10.1109/IJCB48548.2020.9304865 (IEEE, 2020).

  89. Babnik, Z. & Struc, V. Assessing bias in face image quality assessment. In 30th European Signal Processing Conference (EUSIPCO), https://doi.org/10.23919/EUSIPCO55093.2022.9909867 (IEEE, 2020).

Download references

Challenges for automated face recognition systems (2024)
Top Articles
Latest Posts
Recommended Articles
Article information

Author: Golda Nolan II

Last Updated:

Views: 6032

Rating: 4.8 / 5 (58 voted)

Reviews: 89% of readers found this page helpful

Author information

Name: Golda Nolan II

Birthday: 1998-05-14

Address: Suite 369 9754 Roberts Pines, West Benitaburgh, NM 69180-7958

Phone: +522993866487

Job: Sales Executive

Hobby: Worldbuilding, Shopping, Quilting, Cooking, Homebrewing, Leather crafting, Pet

Introduction: My name is Golda Nolan II, I am a thoughtful, clever, cute, jolly, brave, powerful, splendid person who loves writing and wants to share my knowledge and understanding with you.