The use of AI systems has dramatically improved biometric and surveillance technology. AI-powered systems, machine learning and deep neural networks (DNN) algorithms have accelerated and facilitated the collection and processing of biometric data and started the "second wave" in biometric technologies by developing and evolving traditional biometric systems. Indeed, biometric technologies recognise individuals based on their distinctive physical and behavioural attributes, such as fingerprints, face, voice, and fingerprint, which depend on “verification” and “identification aspects”. AI-powered biometrics accelerate the identification and recognition capacity of biometrics and emerge through behavioural biometrics. Moreover, AI augments biometrics' accuracy and improves biometrics' categorisation functionality, particularly AI-enabled surveillance systems such as real-time facial recognition technologies (FRT) which can be used to profile individuals based on their ethnicity, race, national origin, gender, and other characteristics. It is used for identification and verification purposes and can be found in various settings such as closed-circuit television (CCTV), live facial recognition (LFR), retrospective facial recognition, smart glasses, and police body-worn cameras. In the modern era, facial recognition has evolved into a digitalized process utilizing algorithms and AI to analyze and compare facial features. AI-powered FRT uses automated methods to extract, digitize, and analyze the physical characteristics of a person's face, which is called faceprint and reflects a person's unique structure as a fingerprint. Today, different types of FRTs are increasingly adopted in numerous sectors, especially in public service, from automated border control and physical access control to verifying the identity of individuals and even collecting student faceprint for the lunch line. All over the world, sales of this technology have been soaring since 2015, however, normalizing biometrics surveillance with the global COVID-19 epidemic has accelerated the use of these technologies in various public services and raised questions about the legitimate use of AI-enabled surveillance technologies. The proliferation of automated facial recognition technologies (AFRT) in law enforcement agencies, smart-predictive policing, health dataveillance and migration-mobility control has granted states unprecedented capabilities to monitor their citizens. This trend is evident not only in autocratic regimes but also in more advanced democracies, raising concerns about its impact on fundamental principles such as due process, free expression, privacy, and discrimination. In the current global context, where democratic values are under threat, the unregulated use of AI surveillance poses a risk of further eroding the rule of law and favoring illiberal governments, especially in environments where checks and balances are already weakened. While British culture, norms and law provide strong support for protecting privacy and civil liberty collecting UK citizens' biometric data without consent, dataveillance, and deploying biometric systems in the public sphere are controversial issues. The UK government massively operates different forms of FRTs, including real-time FRT in various public services. Therefore this research explores the reining of FRTs, it draws attention to public trust in technology and regulation in the UK. For this, the project utilizes a mixed research of qualitative and quantitative methods. The qualitative part of the project will focus on the content analysis of AI policy documents and conduct elite interviews with UK policy-makers, regulators and researchers. Further, a quantitative section involves an online survey experiment to measure public acceptance of the UK government's use of FRT for surveillance purposes. It also aims to assess public trust in the current regulation on the existing law and policies on the agenda. The regulation of FRT in the UK and elsewhere should address the “pacing problem”, which refers to the gap between the rapid development and diffusion of the technology and the slow adaptation and response of the legal and ethical frameworks. The calibration of FRT regulations to address the intricacies of temporal dynamics assumes a paramount role, effectively forestalling the advent of a digital panopticon or an Orwellian Big Brother epoch characterized by massive surveillance. The UK’s experience with FRT regulation demonstrates the importance of public trust as a key factor for the legitimacy and acceptability of the technology.