Welcome to Francis Academic Press

Academic Journal of Computing & Information Science, 2023, 6(7); doi: 10.25236/AJCIS.2023.060703.

Facial Recognition Technology: A Comprehensive Overview


Li Qinjun, Cui Tianwei, Zhao Yan, Wu Yuying

Corresponding Author:
Li Qinjun

School of Electronic Information and Artificial Intelligence, Shaanxi University of Science & Technology, Xi'an, China


This paper provides an extensive review of facial recognition technology, tracing its historical evolution, exploring its functioning and applications, discussing the challenges it presents, and contemplating future prospects. The technology's inception and advancement are traced from its early stages to the current state, highlighting the key developments that have shaped its progression. An exploration of various types of facial recognition systems, including 2D, 3D, and thermal, underscores the diversity and complexity of this technology. A detailed explanation of how facial recognition works is provided, outlining the processes of data acquisition, face detection, feature extraction, and matching. We further delve into the broad array of its applications across multiple domains, such as security and surveillance, smartphone authentication, social media, healthcare, and retail. Despite the impressive benefits and applications of facial recognition technology, it also presents notable challenges. These include accuracy concerns, privacy and ethical implications, and the need for comprehensive regulatory frameworks. The paper concludes with a forward-looking discussion on the future of facial recognition technology, considering potential innovations and growth predictions. This review provides a comprehensive understanding of facial recognition technology, underscoring its relevance in our digitally driven world and the implications it holds for the future.


Facial recognition, Applications, Challenges, Future prospects

Cite This Paper

Li Qinjun, Cui Tianwei, Zhao Yan, Wu Yuying. Facial Recognition Technology: A Comprehensive Overview. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 7: 15-26. https://doi.org/10.25236/AJCIS.2023.060703.


[1] Kostka, G., Steinacker, L., & Meckel, M. (2021). Between security and convenience: Facial recognition technology in the eyes of citizens in China, Germany, the United Kingdom, and the United States. Public Understanding of Science, 30(6), 671-690.

[2] Smith, A. (2019). More than half of US adults trust law enforcement to use facial recognition responsibly. Pew Research Center, 5.

[3] Kaplan, A. M., & Haenlein, M. (2012). Social media: back to the roots and back to the future. Journal of systems and information technology, 14(2), 101-104.

[4] Mohammed, M. N., Syamsudin, H., Al-Zubaidi, S., AKS, R. R., & Yusuf, E. (2020). Novel COVID-19 detection and diagnosis system using IOT based smart helmet. International Journal of Psychosocial Rehabilitation, 24(7), 2296-2303.

[5] Shankar, V., Kalyanam, K., Setia, P., Golmohammadi, A., Tirunillai, S., Douglass, T., ... & Waddoups, R. (2021). How technology is changing retail. Journal of Retailing, 97(1), 13-27.

[6] Vazquez-Fernandez, E., & Gonzalez-Jimenez, D. (2016). Face recognition for authentication on mobile devices. Image and Vision Computing, 55, 31-33.

[7] Ciftci, O., Choi, E. K. C., & Berezina, K. (2021). Let’s face it: are customers ready for facial recognition technology at quick-service restaurants? International Journal of Hospitality Management, 95, 102941.

[8] Liu, Y. L., Yan, W., & Hu, B. (2021). Resistance to facial recognition payment in China: The influence of privacy-related factors. Telecommunications Policy, 45(5), 102155.

[9] Van Noorden, R. (2020). The ethical questions that haunt facial-recognition research. Nature, 587(7834), 354-359.

[10] Dey, D. (2017). Growing importance of machine learning in compliance and regulatory reporting. European Journal of Multidisciplinary Studies, 2(7), 255-258.

[11] Bledsoe, W. W. (1966). Man-machine facial recognition. Panoramic Research Inc., Palo Alto, CA.

[12] Goldstein, A. J., Harmon, L. D., & Lesk, A. B. (1971). Identification of human faces. Proceedings of the IEEE, 59(5), 748-760.

[13] Sirovich, L., & Kirby, M. (1987). Low-dimensional procedure for the characterization of human faces. Josa a, 4(3), 519-524.

[14] Turk, M., & Pentland, A. (1991). Eigenfaces for recognition. Journal of cognitive neuroscience, 3(1), 71-86.

[15] Belhumeur, P. N., Hespanha, J. P., & Kriegman, D. J. (1997). Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Transactions on pattern analysis and machine intelligence, 19(7), 711-720.

[16] Taigman, Y., Yang, M., Ranzato, M. A., & Wolf, L. (2014). Deepface: Closing the gap to human-level performance in face verification. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1701-1708).

[17] Schroff, F., Kalenichenko, D., & Philbin, J. (2015). Facenet: A unified embedding for face recognition and clustering. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 815-823).

[18] Adjabi, I., Ouahabi, A., Benzaoui, A., & Taleb-Ahmed, A. (2020). Past, present, and future of face recognition: A review. Electronics, 9(8), 1188.

[19] Sengupta, S., Chen, J. C., Castillo, C., Patel, V. M., & Jacobs, D. W.. (2016). Frontal to profile face verification in the wild. 2016 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE.

[20] Zhou, S., Xiao, S. (2018). 3D face recognition: a survey. Hum. Cent. Comput. Inf. Sci. 8, 35.

[21] Luo, J., Hu, F., & Wang, R. (2019). 3D face recognition based on deep learning. In 2019 IEEE International Conference on Mechatronics and Automation (ICMA) (pp. 1576-1581). IEEE.

[22] Rajpurkar, O. M., Kamble, S. S., Nandagiri, J. P., & Bide, P. J. (2020). A Survey on Engagement and Emotion Analysis in Theatre using Thermal Imaging. 2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA).

[23] Van Natta, M., Chen, P., Herbek, S., Jain, R., Kastelic, N., Katz, E., ... & Vattikonda, N. (2020). The rise and regulation of thermal facial recognition technology during the COVID-19 pandemic. Journal of Law and the Biosciences, 7(1), lsaa038. 

[24] Zhang, Y. , Shao, J. , Ouyang, D. , & Shen, H. T. . (2018). Person Re-identification Using Two-Stage Convolutional Neural Network. 2018 24th International Conference on Pattern Recognition (ICPR).

[25] Lu, C. , & Tang, X. . (2014). Surpassing human-level face verification performance on lfw with gaussianface. Computer Science.

[26] Bashbaghi, S. , Granger, E. , Sabourin, R. , & Parchami, M. . (2018). Deep Learning Architectures for Face Recognition in Video Surveillance. 10.48550/arXiv.1802.09990.

[27] Grgic, M. , Delac, K. , & Grgic, S. . (2011). Scface – surveillance cameras face database. Multimedia Tools & Applications, 51(3), 863-879.

[28] Heng, W. , Jiang, T. , & Gao, W. . (2018). How to assess the quality of compressed surveillance videos using face recognition. IEEE Transactions on Circuits and Systems for Video Technology, PP, 1-1.

[29] Ehatisham-ul-Haq, Muhammad, Azam, Muhammad, Awais, & Naeem, et al. (2018). Continuous authentication of smartphone users based on activity pattern recognition using passive mobile sensing. Journal of Network & Computer Applications.

[30] Wasnik, P. , Raja, K. B. , Raghavendra, R. , & Busch, C. . (2017). Assessing face image quality for smartphone based face recognition system. International Workshop on Biometrics & Forensics. IEEE.

[31] Mi, C. , Xu, R. , & Lin, C. T. . (2019). Real-time recognition of smartphone user behavior based on prophet algorithms.

[32] Bud, & Andrew. (2018). Facing the future: the impact of apple faceid. Biometric Technology Today, 2018(1), 5-7.

[33] Hebbale, S. G. , Mukherjee, A. , & Seal, A. . (2019). People Search on Social Media Platform Using Face Recognition. SoutheastCon 2019.

[34] Afra, S. , & Alhajj, R. . (2020). Early warning system: from face recognition by surveillance cameras to social media analysis to detecting suspicious people. Physica, A. Statistical mechanics and its applications, 540.

[35] Indrawan, P., Budiyatno, S., Ridho, N. M., & Sari, R. F. (2013). Face recognition for social media with mobile cloud computing. International Journal on Cloud Computing: Services and Architecture, 3(1), 23-35.

[36] Norval, A., & Prasopoulou, E. (2017). Public faces? A critical exploration of the diffusion of face recognition technologies in online social networks. New media & society, 19(4), 637-654.

[37] Cherepanova, V., Goldblum, M., Foley, H., Duan, S., Dickerson, J., Taylor, G., & Goldstein, T. (2021). Lowkey: Leveraging adversarial attacks to protect social media users from facial recognition. arXiv preprint arXiv:2101.07922.

[38] Libby, C., & Ehrenfeld, J. (2021). Facial recognition technology in 2021: masks, bias, and the future of healthcare. Journal of Medical Systems, 45(4), 39.

[39] Shamova, U. (2020). Face recognition in healthcare: general overview. Язык в сфере профессиональной коммуникации.—Екатеринбург, 2020, 748-752.

[40] Sardar, A., Umer, S., Rout, R. K., Wang, S. H., & Tanveer, M. (2023). A secure face recognition for IoT-enabled healthcare system. ACM Transactions on Sensor Networks, 19(3), 1-23.

[41] Praveen, G. B., & Dakala, J. (2020, January). Face recognition: Challenges and issues in smart city/environments. In 2020 International Conference on COMmunication Systems & NETworkS (COMSNETS) (pp. 791-793). IEEE.

[42] Masud, M., Muhammad, G., Alhumyani, H., Alshamrani, S. S., Cheikhrouhou, O., Ibrahim, S., & Hossain, M. S. (2020). Deep learning-based intelligent face recognition in IoT-cloud environment. Computer Communications, 152, 215-222.

[43] Alhussein, M. (2016). Automatic facial emotion recognition using weber local descriptor for e-Healthcare system. Cluster Computing, 19, 99-108.

[44] Wright, E. (2018). The future of facial recognition is not fully known: Developing privacy and security regulatory mechanisms for facial recognition in the retail sector. Fordham Intell. Prop. Media & Ent. LJ, 29, 611.

[45] Mansfield-Devine, S. (2013). Biometrics in retail. Biometric Technology Today, 2013(9), 5-8.

[46] Dijmărescu, I., Iatagan, M., Hurloiu, I., Geamănu, M., Rusescu, C., & Dijmărescu, A. (2022). Neuromanagement decision making in facial recognition biometric authentication as a mobile payment technology in retail, restaurant, and hotel business models. Oeconomia Copernicana, 13(1), 225-250.

[47] Gao, J., Rong, Y., Tian, X., & Yao, Y. (2023). Improving Convenience or Saving Face? An Empirical Analysis of the Use of Facial Recognition Payment Technology in Retail. Information Systems Research.

[48] Martínez-Mascorro, G. A., Abreu-Pederzini, J. R., Ortiz-Bayliss, J. C., & Terashima-Marín, H. (2020). Suspicious behavior detection on shoplifting cases for crime prevention by using 3D convolutional neural networks. arXiv preprint arXiv:2005.02142. 

[49] Martínez-Mascorro, G. A., Abreu-Pederzini, J. R., Ortiz-Bayliss, J. C., Garcia-Collantes, A., & Terashima-Marín, H. (2021). Criminal intention detection at early stages of shoplifting cases by using 3D convolutional neural networks. Computation, 9(2), 24.

[50] Adini, Y. , & Moses, Y. . (1997). Face recognition: the problem of compensating for changes in illumination direction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7), P.721-732.

[51] Nomi, J. S., Rhodes, M. G., & Cleary, A. M. (2013). Emotional facial expressions differentially influence predictions and performance for face recognition. Cognition & emotion, 27(1), 141-149.

[52] Chen, B. C., Chen, C. S., & Hsu, W. H. (2014). Cross-age reference coding for age-invariant face recognition and retrieval. In Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part VI 13 (pp. 768-783). Springer International Publishing.

[53] Park, J. S., Oh, Y. H., Ahn, S. C., & Lee, S. W. (2005). Glasses removal from facial image using recursive error compensation. IEEE transactions on pattern analysis and machine intelligence, 27(5), 805-811.

[54] Herlitz, A., & Lovén, J. (2013). Sex differences and the own-gender bias in face recognition: A meta-analytic review. Visual Cognition, 21(9-10), 1306-1336.

[55] Bowyer, K. W. (2004). Face recognition technology: security versus privacy. IEEE Technology and society magazine, 23(1), 9-19.

[56] Senior, A. W., & Pankanti, S. (2011). Privacy protection and face recognition. Handbook of face recognition, 671-691.

[57] Almeida, D., Shmarko, K., & Lomas, E. (2022). The ethics of facial recognition technologies, surveillance, and accountability in an age of artificial intelligence: a comparative analysis of US, EU, and UK regulatory frameworks. AI and Ethics, 2(3), 377-387.

[58] Eneman, M., Ljungberg, J., Raviola, E., & Rolandsson, B. (2022). The sensitive nature of facial recognition: Tensions between the Swedish police and regulatory authorities. Information Polity, (Preprint), 1-14.

[59] Wright, E. (2018). The future of facial recognition is not fully known: Developing privacy and security regulatory mechanisms for facial recognition in the retail sector. Fordham Intell. Prop. Media & Ent. LJ, 29, 611.

[60] Selinger, E., & Hartzog, W. (2020). The inconsentability of facial surveillance. Loy. L. Rev., 66, 33.

[61] Hu, Z., Gui, P., Feng, Z., Zhao, Q., Fu, K., Liu, F., & Liu, Z. (2019). Boosting depth-based face recognition from a quality perspective. Sensors, 19(19), 4124.

[62] Hernandez-Ortega, J., Galbally, J., Fiérrez, J., & Beslay, L. (2020). Biometric quality: Review and application to face recognition with faceqnet. arXiv preprint arXiv:2006.03298.

[63] Nech, A., & Kemelmacher-Shlizerman, I. (2017). Level playing field for million scale face recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 7044-7053).

[64] Ranjan, R., Sankaranarayanan, S., Bansal, A., Bodla, N., Chen, J. C., Patel, V. M., ... & Chellappa, R. (2018). Deep learning for understanding faces: Machines may be just as good, or better, than humans. IEEE Signal Processing Magazine, 35(1), 66-83.

[65] Buolamwini, J., & Gebru, T. (2018, January). Gender shades: Intersectional accuracy disparities in commercial gender classification. In Conference on fairness, accountability and transparency (pp. 77-91). PMLR.

[66] De-Arteaga, M., Romanov, A., Wallach, H., Chayes, J., Borgs, C., Chouldechova, A., ... & Kalai, A. T. (2019, January). Bias in bios: A case study of semantic representation bias in a high-stakes setting. In proceedings of the Conference on Fairness, Accountability, and Transparency (pp. 120-128).

[67] Shokri, R., & Shmatikov, V. (2015, October). Privacy-preserving deep learning. In Proceedings of the 22nd ACM SIGSAC conference on computer and communications security (pp. 1310-1321).

[68] Fussey, P., & Murray, D. (2019). Independent report on the London Metropolitan Police Service’s trial of live facial recognition technology.

[69] Bousa, M., Anagnostopoulos, G., del Corro, E., Drogowska, K., Pekarek, J., Kavan, L., ... & Frank, O. (2016). Stress and charge transfer in uniaxially strained CVD graphene. physica status solidi (b), 253(12), 2355-2361.

[70] Jain, A. K., Ross, A. A., Nandakumar, K., Jain, A. K., Ross, A. A., & Nandakumar, K. (2011). Face recognition (pp. 97-139). Springer US.

[71] Grother, P. J., Grother, P. J., Ngan, M., & Hanaoka, K. (2014). Face recognition vendor test (FRVT). US Department of Commerce, National Institute of Standards and Technology.

[72] Liu, S., Liu, L., Tang, J., Yu, B., Wang, Y., & Shi, W. (2019). Edge computing for autonomous driving: Opportunities and challenges. Proceedings of the IEEE, 107(8), 1697-1716.