Authors : Alessandra M. Arpon; Aman Mohamed Ashique; Zeinab Mahmoud; Maham Nadeem

Volume/Issue : Volume 11 - 2026, Issue 9 - September

Google Scholar : https://tinyurl.com/4e6v5szw

DOI : https://doi.org/10.38124/ijisrt/26sep704

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : No two people look the same, even though human faces share many basic characteristics. What still defines diversity today is the variances in the form, size, and color of each human being. This paper emphasizes the significance of facial recognition since it heavily impacts a person's socialization process and neurodevelopment throughout the course of their lifetime. People depend on this brain function because human faces are utilized to extract socially significant information that is crucial for daily interactions and maintaining personal relationships. How the brain recognizes faces is still not completely known to this day. Scientists have conducted studies using rhesus monkeys and discovered that a set of neurons in the temporal lobe, particularly the Fusiform Face Area, is involved in recognizing facial features. These sets of neurons work to recognize faces; however, the exact position and number of these neurons are unidentified. The following topics within the sectors of facial recognition technology are covered, how facial recognition has paved its way to technology and the betterment of society. Moreover, covering the early development of facial recognition technology, which was initially developed by Woodrow Wilson Bledsoe with the use of computers in 1963. The findings of this paper indicate that while technological innovations in the field of facial recognition have demonstrated an increase in safety and security in every sector, their lack of transparency and inherent flaws can create problems and mistrust among members of society. Because of its many applications and significance, facial recognition is a topic that needs further investigation. The goal of this paper is to provide future scholars and researchers focusing on the effects of facial recognition technology with insights into a variety of sectors.

Keywords : Facial Recognition, Machine Learning, Facial Recognition Technology.

References :

1. Tsao, D. Y ., And Livingstone, M. S. (2008). Mechanisms of face perception. Annu. Rev. Neurosci., 31, 411-437. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2629401/
2. Lopatina, O. L., Komleva, Y . K., Gorina, Y. V., Higashida, H., And Salmina, A. B. (2018). Neurobiological Aspects of Face Recognition: The Role of Oxytocin. Frontiers in Behavioral Neuroscience, 12, 399601. https://doi.org/10.3389/fnbeh.2018.00195
3. Facial Recognition and the Brain. (2020). STEM explained. Let's Talk Science. https://letstalkscience.ca/educational-resources/stem-explained/facial-recognition-and-brain#:~:text=Faces%20are%20pretty%20special!,slip%20up%20once%20and%20awhile
4. Gauthier, I., And Tarr, M. J. (1997). Becoming a “Greeble” expert: Exploring mechanisms for face recognition. Vision research, 37(12), 1673-1682.
5. Liu, K., Chen, C. Y ., Wang, L. S., Jo, H., And Kung, C. C. (2023). Is increased activation in the fusiform face area to Greebles a result of appropriate expertise training or caused by Greebles' face likeness?. Frontiers in Neuroscience, 17, 1224721.
6. Freiwald, W. (2021). Scientists discover a new class of neurons for remembering faces. The Rockefeller University.https://www.rockefeller.edu/news/30666-scientists-discover-new-class-neurons-remembering-faces/
7. Perrett, D. I., Rolls, E. T., & Caan, W. (1982). Visual neuronest responsive to faces in the monkey temporal cortex. Experimental brain research, 47(3), 329–342. https://doi.org/10.1007/BF00239352
8. Febery, J. (2018). Faces in the Brain: The Discovery of a Neuronal Subpopulation Selective for Face Recognition. Journal of Undergraduate Neuroscience Education, 16(2), R57.
9. Chang, L., & Tsao, D. Y. (2017). The code for facial identity in the primate brain. Cell, 169(6), 1013-1028.
10. McQuate, S. (2017). Neuroscientists rethink how the brain recognizes faces. Nature. https://www.nature.com/articles/nature.2017.22091
11. Tolba, A. S., El-Baz A. H., and El-Harby, A. A., (2006). Face recognition: A literature review. International Journal of Signal Processing, 2(2), 88-103.
12. Bledsoe, W. W. (1964). Facial recognition project report. Submitted to King-Hurley Research Group by Panoramic Research Inc., Palo Alto. https://archive.org/details/firstfacialrecognitionresearch/SecondReport/
13. Lydick, N. (n.d.). A Brief Overview of Facial Recognition. Intro to Facial Recognition. https://www.eecs.umich.edu/courses/eecs487/w07/sa/pdf/nlydick-facial-recognition.pdf
14. Heo, J., Abidi, B., Paik, J., And Abidi, M. (2003). Face recognition: evaluation report for FaceIt® Identification and Surveillance. Proc. of SPIE 6th International Conference on Quality Control by Artificial Vision, V ol. 5132, pp. 551-558.
15. Den Uyl, M. J., And Van Kuilenberg, H. (2005). The FaceReader: Online Facial Expression Recognition. In Proceedings of measuring behaviour (V ol. 30, No. 2, pp. 589-590).
16. Yuvchenko, K., Yesilevskyi, V., & Sereda, O. (2022). Human emotion recognition system using deep learning algorithms. Innovative Technologies and Scientific Solutions for Industries, (3 (21)), 60-69.
17. Riaz, W., Ji, J., & Ullah, A. (2025). TriViT-Lite: A Compact Vision Transformer–MobileNet Model with Texture-Aware Attention for Real-Time Facial Emotion Recognition in Healthcare. Electronics, 14(16), 3256.
18. Martin, N. M. (2019). What Are Important Ethical Implications of Using Facial Recognition Technology in Health Care? AMA Journal of Ethics, 21(2), 180-187.
19. Kong, X., Wang, Z., Sun, J., Qi, X., Ding, X., And Qiu, Q. (2024). Facial Recognition for Disease Diagnosis Using a Deep Learning Convolutional Neural Network: A Systematic Review and Meta-Analysis. J Fam Med, 11(1), 1345.
20. Hasan Amin, M., Neoaz, N. (2025). From Theory to Implementation: Optimizing AI-Driven Depression Detection Using Facial Recognition, EEG, and Algorithmic Innovations, 1 (1), https://doi.org/10.70445/gtst.1.1.2025.30-39
21. Tanveer, H., Ahmed, S., & Khan, M. (2025). Advancing mental health AI with facial recognition and EEG brainwave analysis: Objective detection of anxiety and bipolar disorder. International Journal of Medical and All Body Health Research, 37, 30–45.
22. Munsif, M., Sajjad, M., Ullah, M., Tarekegn, A. N., Cheikh, F. A., Tsakanikas, P., & Muhammad, K. (2024). Optimized efficient attention-based network for facial expressions analysis in neurological health care. Computers in Biology and Medicine, 179, 108822.
23. Islam, M. M., Nooruddin, S., Karray, F., & Muhammad, G. (2024). Enhanced multimodal emotion recognition in healthcare analytics: A deep learning based model-level fusion approach. Biomedical Signal Processing and Control, 94, 106241. https://doi.org/10.1016/j.bspc.2024.106241
24. Jeon, B., Jeong, B., Jee, S., Huang, Y., Kim, Y., Park, G. H., ... And Choi, T. H. (2019). A facial recognition mobile app for patient safety and biometric identification: design,development, and validation. JMIR mHealth and uHealth, 7(4), e11472. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6475824/
25. Srinivasan, S., Raja, R., Jehan, C., Murugan, S., Srinivasan, C., & Muthulekshmi, M. (2024, March). IoT-enabled facial recognition for smart hospitality for contactless guest services and identity verification. In 2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions)(ICRITO) (pp. 1-6). IEEE.
26. Gurovich, Y., Hanani, Y., Bar, O., Nadav, G., Fleischer, N., Gelbman, D., ... & Gripp, K. W. (2019). Identifying facial phenotypes of genetic disorders using deep learning. Nature medicine, 25(1), 60-64.
27. Crumpler, W. (2020). “How Accurate are Facial Recognition Systems – and Why Does It Matter?” Strategic Technologies Blog | CSIS. https://www.csis.org/blogs/strategic-technologies-blog/how-accurate-are-facial-recognition-systems-and-why-does-it#main-content
28. Thakur, S., khan, N. (2024). Security Implications of IoT-Enabled Mobile Net Facial Recognition System. Journal of Intelligent Systems and Internet of Things, Volume 13(Issue 2), 231-244. DOI: https://doi.org/10.54216/JISIoT.130219
29. Amri, H. (2025). Real-Time Face Attendance System Using CNN Mobilenet and MTCNN. JURNAL TEKNOLOGIDAN OPEN SOURCE, 8(1), 164 - 179. https://doi.org/10.36378/jtos.v8i1.4403
30. Aboluhom, A. A. A., & Kandilli, I. (2025). Real-time facial recognition via multitask learning on raspberry Pi. Scientific reports, 15(1), 28467. https://doi.org/10.1038/s41598-025-97490-6
31. Zhang, W.-K., & Kang, M.-J. (2019). Factors affecting the use of facial-recognition payment: An example of Chinese consumers. IEEE Access, 7, 154360–154374.  DOI: 10.1109/ACCESS.2019.2927705
32. 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. https://doi.org/10.1177/09636625211001555
33. Yanisky-Ravid, S., & Fleming, K. (2023). The tripartite model of facial recognition: Bridging the gap between privacy, public safety, technology and the Fourth and First Amendments. Notre Dame Journal of Law, Ethics & Public Policy, 37(1), 159–. https://scholarship.law.nd.edu/ndjlepp/vol37/iss1/6/
34. Kim, C. (2023). Pulling back the veil: exposing pernicious uses of facial recognitiontechnology. Washington University Global Studies Law Review. https://journals.library.wustl.edu/globalstudies/article/id/8870/
35. Gültekin-Várkonyi, G. (2024). Navigating data governance risks: Facial recognition in law enforcement under EU legislation. Internet Policy Review, 13(3). https://doi.org/10.14763/2024.3.1798
36. Lai, X., & Rau, P. L. P. (2021). Has facial recognition technology been misused? A public perception model of facial recognition scenarios. Computers in Human Behavior, 124, 106894. https://doi.org/10.1016/j.chb.2021.106894
37. Buolamwini, J., & Gebru, T. (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. Proceedings of Machine Learning Research, 81, 77–91. https://www.media.mit.edu/publications/gender-shades-intersectional-accuracy-disparities-in-commercial-gender-classification/
38. Simmler, M., & Canova, G. (2024). Facial recognition technology in law enforcement: Regulating data analysis of another kind. Computer Law & Security Review, 56, 106092. https://doi.org/10.1016/j.clsr.2024.106092
39. Wang, X., et al. (2024). Beyond surveillance: Privacy, ethics, and regulations in facial recognition technology. Frontiers in Big Data, 7, 1337465. https://doi.org/10.3389/fdata.2024.1337465
40. Kanwisher, N., & Yovel, G. (2006). The fusiform face area: a cortical region specialized for the perception of faces. Philosophical Transactions of the Royal Society B: Biological Sciences, 361(1476), 2109.
41. Yu, G., Katz, L.N., Quaia, Messinger, & Krauzlis, R.J. (2024). Short-latency preference for faces in primate superior colliculus depends on visual cortex. Neuron, 112(16), 2814–2822.e4.
42. Haxby, J. V., Hoffman, E. A., & Gobbini, M. I. (2000). The distributed human neural system for face perception. Trends in cognitive sciences, 4(6), 223-233.
43. Rossion, B., Jacques, C., & Jonas, J. (2024). The anterior fusiform gyrus: The ghost in the cortical face machine. Neuroscience & Biobehavioral Reviews, 158, 105535.
44. Pessoa, L., & Adolphs, R. (2010). Emotion processing and the amygdala: from a'low road'to'many roads' of evaluating biological significance. Nature reviews neuroscience, 11(11), 773-782.
45. Gepshtein, S., Wang, Y., He, F., Diep, D., & Albright, T. D. (2020). A perceptual scaling approach to eyewitness identification. Nature communications, 11(1), 3380.
46. Aston, T., Machtsiras, G., & Teixeira-Dias, F. (2026). Deep learning approaches for head pose estimation in sports impacts. Sports engineering, 29(1), 17. https://doi.org/10.1007/s12283-026-00552-9
47. Cuellar, M., To, H. K. J., & Mehrotra, A. (2025). Accuracy and fairness of facial recognition technology in low-quality police images: An experiment with synthetic faces. https://arxiv.org/pdf/2505.14320?
48. Taliaferro, D., & Davis. (2025). Facing the facts: The efficacy of police facial recognition (Unpublished manuscript). University of Virginia. https://economics.virginia.edu/sites/economics.as.virginia.edu/files/2025-05/Taliaferro%2C%20Davis.pdf
49. Ahmed, H. S. A. (2022,). Facial Recognition Technology and Privacy Concerns. Isaca; ISACA. https://www.isaca.org/resources/news-and-trends/newsletters/atisaca/2022/volume-51/facial-recognition-technology-and-privacy-concerns
50. Andrewi, B. (2021). Facial recognition technology in schools - House of Lords Library. House of Lords Library. https://lordslibrary.parliament.uk/facial-recognition-technology-in-schools/
51. Lau, P. L. (n.d.). Facial recognition in schools: here are the risks to children. The Conversation.https://theconversation.com/facial-recognition-in-schools-here-are-the-risks-to-children-170341
52. Goldberg, H. (2022). Growing Brains, Nurturing Minds—Neuroscience as an Educational Tool to Support Students’ Development as Life-Long Learners. Brain Sciences, 12(12), 1622.
53. Goldy, R. F., Boomika, N., & Danis Swetha, A. (2025). Smart Attendance using Face Recognition. International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE). https://doi.org/10.17148/IJARCCE.2025.14483
54. Sheela, S. D., Byalal, R. M., & Murthy, R. V. (2025). Face Recognition Attendance Management System. International Research Journal of Modernization in Engineering Technology and Science (IRJMETS), 7(4). https://doi.org/10.56726/IRJMETS72796.