From Algorithms to Applications: A Review of AI-Based Face Recognition and Identity Verification
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Abstract
Face recognition and identity verification are now critical components of current security and verification technology. The main objective of this review is to identify the most important deep learning techniques that have contributed to the improvement in the accuracy and reliability of facial recognition systems, as well as highlighting existing problems and potential future research areas. An extensive literature review was conducted with the assistance of leading scientific databases such as IEEE Xplore, ScienceDirect, and SpringerLink and covered studies from the period 2015 to 2024. The studies of interest were related to the application of deep neural networks, i.e., CNN, Siamese, and Transformer-based models, in face recognition and identity verification systems. Deep learning-based approaches have been shown through cross-sectional studies to improve recognition accuracy under diverse environmental and demographic conditions. Anti-counterfeiting (Anti-Spoofing) and real presence detection features integrated into systems have likewise enhanced system security against advanced attacks such as 3D masks, false images and videos, and Deepfake technology. Future trends point to the need to develop deep, multi-sensory and interpretable learning models, and adopt learning strategies based on limited data, while adhering to legal and ethical frameworks to ensure fairness and
transparency.
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References
S. Kolekar, P. Patil, P. Barge, and T. Kosare, “A comprehensive study on artificial intelligence-based face recognition technologies,” in Springer International Publishing, pp. 249–263, 2023, doi: 10.1007/978-981-99-6586-1_17.
M. Ramaswamy, “AI-enhanced secure identity verification for financial services,” Int. J. Multidiscip. Res., vol. 6, no. 6, 2024, doi: 10.36948/ijfmr.2024.v06i06.26589.
P. Khare and S. Arora, “Artificial intelligence-based biometric authentication systems for facial recognition and identification,” in Proc. ICEECT, pp. 1–7, 2024, doi: 10.1109/iceect61758.2024.10738912.
W. Mo, “Method and device for identity verification based on face recognition,” 2018.
M. K. Pasupuleti, “AI-enabled multimodal biometrics: Advancing security with facial, voice, and behavioral integration,” pp. 1–9, 2025, doi: 10.62311/nesx/77579.
K. Das, H. Bordoloi, and K. K. Sarma, “Face recognition based verification with reinforced decision support,” in Proc. Int. Conf. Computer and Communication Technology, pp. 759–763, 2010, doi: 10.1109/ICCCT.2010.5640440.
T. Sethukarasi, S. Bharath, K. Sujatha, G. B. Bharathi, and S. Saravanakumar, “A smart face biometric verification oriented attendance handling system using artificial intelligence,” pp. 1–6, 2023, doi: 10.1109/icses60034.2023.10465453.
S. K. Park, “System and method for decentralized identifier based on face recognition,” 2020.
A. Pandit, R. Nikalje, N. Vishwakarma, V. Vishwasrao, and T. Khose, “Face authentication using MTCNN and FaceNet,” Int. J. Res. Appl. Sci. Eng. Technol., 2023, doi: 10.22214/ijraset.2023.56679.
R. H. Hasan, “A new strategy to modify Hopfield by using XOR operation,” Baghdad Sci. J., vol. 21, no. 9, art. 17, 2024, doi: 10.21123/bsj.2024.9238.
R. Yu, X. Zhang, Y. Zhang, J. Song, K. Liu, and Q. Miao, “Design and implementation of identity verification software based on deep learning,” Int. J. Digit. Crime Forensics, vol. 14, no. 3, pp. 1–15, 2022, doi: 10.4018/ijdcf.315796.
A. Gautam, J. Ilaparti, S. Rajvanshi, N. Badhani, D. Gangodkar, and Y. Lohumi, “Masked face detection and reconstruction for identity verification,” pp. 1–7, 2024, doi: 10.1109/ic2e362166.2024.10827430.
P. Ahlawat, N. Kaur, C. Kaur, S. Kumar, and H. K. Sharma, “Deep learning based face recognition system for automated identification,” in Springer Science+Business Media, pp. 60–72, 2023, doi: 10.1007/978-3-031-48781-1_6.
H.-C. Wang and J. Wu, “A machine learning-based secure face verification scheme and its applications to digital surveillance,” 2024, doi: 10.48550/arxiv.2410.21993.
J. Xiao and S. H. Wang, “A unified identity authentication method based on face information,” 2023, doi: 10.1145/3656766.3656786.
W. Nie and M. Li, “Deep learning-based face recognition and face verification supervised learning method,” 2018.
T.-K. Wang, Y.-H. Lin, and K.-P. Li, “Application of AI face recognition technology in swipe card attendance systems for hospitals,” Proc. Eng. Technol. Innov., vol. 21, pp. 1–9, 2022, doi: 10.46604/peti.2022.8984.
X. You, X. Zhao, and J. Lu, “Identity security verification method and system based on identity documents and face recognition,” 2018.
M. Thalor and O. Gaikwad, “Facial recognition attendance monitoring system using deep learning techniques,” Int. J. Integr. Sci. Technol., vol. 2, no. 1, pp. 45–52, 2024, doi: 10.59890/ijist.v2i1.1290.
A. Okumura, S. Handa, T. Hoshino, N. Tokunaga, and M. Kanda, “Identity verification using face recognition improved by managing check-in behavior of event attendees,” in Springer, Cham, pp. 291–304, 2019, doi: 10.1007/978-3-030-39878-1_26.
J. Xu, Y. Xie, and J. Wang, “Identity verification and recognition method based on face image,” 2012.
V. Gujar, “Integrating facial recognition technology in IndoAI app with eKYC case study & future of AI cameras in banking,” Int. J. Sci. Res., 2023, doi: 10.21275/sr231108193207.
R. Ravindran, S. Udayakumar, Y. Revanth, and K. Namitha, “Enhancing access control: A biometric approach with facial recognition and near-field communication integration,” pp. 263–268, 2024, doi: 10.1109/icici62254.2024.00051.
“Privacy threats in facial recognition-based identity verification,” Int. J. Adv. Res. Sci. Commun. Technol., pp. 569–572, 2023, doi: 10.48175/ijarsct-11686.
R. K. V. Satish, S. A. Ahmed, S. H. Jadhav, M. N. G. Prasad, J. G. Thatipudi, V. Tejasri, and J. Divya, “Futuristic authentication: Development of an artificial intelligence based enhanced face detection and recognition system,” 2024, doi: 10.1109/icict60155.2024.10544692.
“Top advantages of using facial recognition in e-KYC,” Fraud.com, [Online]. Available: https://www.fraud.com/post/e-kyc. [Accessed: Sep. 26, 2025].
“Top 5 advantages of using facial recognition in eKYC,” Finextra, [Online]. Available: https://www.finextra.com/blogposting/27606/top-5-advantages-of-using-facial-recognition-in-ekyc. [Accessed: Sep. 26, 2025].
“Biometrics and financial crime prevention,” SymphonyAI, [Online]. Available: https://www.symphonyai.com/resources/blog/financial-services/biometrics-financial-crime-prevention-regulators. [Accessed: Sep. 26, 2025].
“Facial recognition for KYC solutions,” Vention, [Online]. Available: https://ventionteams.com/solutions/computer-vision/facial-recognition-kyc. [Accessed: Sep. 26, 2025].
“Facial recognition for KYC solutions,” Vention, [Online]. Available: https://ventionteams.com/solutions/computer-vision/facial-recognition-kyc. [Accessed: Sep. 26, 2025].
M. Wang and W. Deng, “Deep face recognition: A survey,” Neurocomputing, vol. 429, pp. 215–244, 2021, doi: 10.1016/j.neucom.2020.10.081.
B. Chen, X. Liao, H. Zhu, Z. Gong, and Y. Li, “A survey of face recognition methods based on deep learning,” Highlights Sci. Eng. Technol., vol. 24, pp. 191–197, 2022, doi: 10.54097/hset.v24i.3921.
Y. Kortli, M. Jridi, A. Al Falou, and M. Atri, “Face recognition systems: A survey,” Sensors, vol. 20, no. 2, p. 342, 2020, doi: 10.3390/s20020342.