Application, Controversy and Governance of Artificial Intelligence Facial Recognition in Public Security

Jiaye Shen; Jiale Zhan

doi:10.54097/2e2mkf95

Authors

Jiaye Shen Shanghai Nanyang Model Private School, 200333, China
Jiale Zhan School of Electronic Information Engineering, Zhuhai College of Science and Technology, Guangdong, 519041, China

DOI:

https://doi.org/10.54097/2e2mkf95

Keywords:

Artificial intelligence; Facial recognition; Public security.

Abstract

With the rapid development of artificial intelligence technology, facial recognition is more extensively applied in the field of public security as an efficient means of identity verification and surveillance. It brings convenience to crime prevention, suspect tracking and emergency response. However, the popularization of this technology has also sparked intense social disputes and governance challenges, mainly focusing on privacy infringement, data security, algorithmic bias, and potential abuse of power. Striking a balance between leveraging technological advantages and safeguarding citizens’ rights has become a key issue. This study classifies, integrates and analyzes the application of artificial intelligence facial recognition in the field of public security by adopting a systematic literature review method. Findings indicate that while its use has expanded across various domains like crime prevention and border control, demonstrating notable efficiency gains, it simultaneously exposes problems such as lagging legal regulations, inconsistent technical standards, and a lack of ethical supervision. The core of the controversy lies in the conflict between technological power and fundamental civil rights. To foster the healthy development of artificial intelligence facial recognition in of public security, a comprehensive governance system integrating technical norms, legal constraints, and ethical guidance is essential.

Downloads

Download data is not yet available.

References

[1] Ali I A, Aiswarya B, Ezedin B, Khaled S. AI-powered biometrics for Internet of Things security: A review and future vision. Journal of Information Security and Applications, 2024, 82: 103748.

[2] Arul S, Kavitha P. Real-time crime monitoring system using deep learning for weapon, behavior, and facial detection. International Journal on Smart Sensing and Intelligent Systems, 2025, 18(1): 20250051.

[3] Face recognition allows vagrants to go home early. Chinese Civil Affairs, 2018, (11): 55.

[4] Chen B, Jiang X G. Convolutional neural network is used for facial feature extraction [J]. Modern Electronic Technology, 2022, 45(18): 182-186.

[5] Li X L, Liu Z K, Yu N H, et al. A region-based dynamic block image retrieval method [J]. Journal of Circuits and Systems, 2002, (01): 47-51.

[6] Ghogale K N, Apare R S, Borhade R H. HyDeepIDF-Net: Hybrid Deep ID FaceNet for Person Re-Identification. Journal of Ambient Intelligence and Humanized Computing, 2025, 16(6-7): 1-34.

[7] Li F, Yan X, Zhang X Y. GPU-based feature face algorithm optimization research [J]. Computer Science, 2021, 48(04): 197-204.

[8] Liu J Y, Wang Y, Wang K, Liu Z. A face template protection scheme based on chaotic map, error correction code and locality sensitive hashing. Cybersecurity, 2025, 8(1): 84.

[9] Byeon H, Shabaz M, Surbakti H, Keshta I, Soni M, Bhatnagar V. Deep learning and encryption algorithms based model for enhancing biometric security for artificial intelligence era. Journal of Ambient Intelligence and Humanized Computing, 2024.

[10] Wu L W, Yang C. IoT device identity authentication method based on rPPG and CNN facial recognition . International Journal of Advanced Computer Science and Applications (IJACSA), 2024, 15(5).