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022 |a 1582-6384 
022 |a 2247-840X 
024 7 |a 10.2478/raft-2025-0024  |2 doi 
035 |a 3224960147 
045 2 |b d20250401  |b d20250630 
084 |a 113225  |2 nlm 
100 1 |a Tical, George-Marius  |u "Andrei Saguna" University, Constanta, Romania 
245 1 |a FACIAL RECOGNITION AND BIOMETRIC SYSTEMS: BENEFITS AND CHALLENGES FOR LAW ENFORCEMENT 
260 |b Nicolae Balcescu  |c 2025 
513 |a Journal Article 
520 3 |a In the digital era, biometric technologies such as facial recognition and fingerprint scanning have become essential for law enforcement, enabling rapid and accurate suspect identification while enhancing investigative efficiency. These technologies offer significant benefits, including crime reduction, minimization of human errors, and resource optimization. However, their use raises major challenges related to data privacy, cybersecurity, and the ethics of surveillance. European regulations, particularly the General Data Protection Regulation (GDPR) and the Artificial Intelligence Act (AI Act), impose strict restrictions on biometric data processing to prevent misuse and excessive surveillance. According to the European Data Protection Board (EDPB) recommendations, the use of facial recognition in public spaces must be justified and limited to exceptional situations. Although biometric technologies can significantly improve public safety, risks associated with algorithmic bias, which may lead to discrimination, as well as the potential misuse of collected data, remain pressing concerns. Therefore, their implementation must be transparent, ethical, and compliant with existing legislation. For the responsible use of these technologies, strict data protection measures, continuous monitoring and auditing of biometric systems, and the development of fairer algorithms are recommended. This approach ensures a balance between the operational efficiency of law enforcement agencies and the protection of fundamental rights of citizens. 
653 |a Physiology 
653 |a Face recognition 
653 |a Accuracy 
653 |a Legislation 
653 |a Surveillance 
653 |a Data processing 
653 |a Law enforcement 
653 |a Biometrics 
653 |a Cybersecurity 
653 |a Public safety 
653 |a Facial recognition technology 
653 |a Privacy 
653 |a Biometric identification 
653 |a Data collection 
653 |a Efficiency 
653 |a Gait 
653 |a Forensic sciences 
653 |a Artificial intelligence 
653 |a Voice recognition 
653 |a Optimization 
653 |a Airports 
653 |a Methods 
653 |a Digital Age 
653 |a Biometric recognition systems 
653 |a Algorithms 
653 |a Criminal investigations 
653 |a Crime prevention 
653 |a General Data Protection Regulation 
653 |a Ethics 
653 |a Human error 
653 |a Computer forensics 
653 |a Security systems 
653 |a Errors 
653 |a Civil rights 
653 |a Public spaces 
653 |a Safety regulations 
653 |a Discrimination 
653 |a Human rights 
653 |a Law enforcement agencies 
653 |a Protection 
653 |a Police 
653 |a Recognition 
653 |a Crime 
653 |a Acknowledgment 
653 |a Minimization 
653 |a Data integrity 
773 0 |t Land Forces Academy Review  |g vol. 30, no. 2 (2025), p. 249-260 
786 0 |d ProQuest  |t ABI/INFORM Global 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3224960147/abstract/embedded/75I98GEZK8WCJMPQ?source=fedsrch 
856 4 0 |3 Full Text  |u https://www.proquest.com/docview/3224960147/fulltext/embedded/75I98GEZK8WCJMPQ?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/3224960147/fulltextPDF/embedded/75I98GEZK8WCJMPQ?source=fedsrch