Analyzing the efficacy of artificial intelligence algorithms in automating forensic facial recognition techniques
Submission: 12 February 2026 | Acceptance: 16 March 2026 | Publication: 28 April 2026
1Dr Mahwish Zeb, 2Khawar Qayyum, 2Sobia Wajid, 2Abbas Ali, 2Noreen Akhtar
1Assistant Professor, Department of forensic Medicine and Toxicology, Ayub Medical College Abbottabad
2PIMS Islamabad
Abstract
Background: Artificial intelligence (AI) has significantly transformed forensic science, particularly in the field of facial recognition. Automated facial recognition systems are increasingly utilized in criminal investigations, border security, and identity verification. Despite rapid technological advancements, concerns regarding accuracy, bias, privacy, and legal admissibility remain significant challenges.
Objective: To evaluate the efficacy of artificial intelligence algorithms in automating forensic facial recognition techniques and to analyze their benefits, limitations, and implications for forensic investigations.
Methods: A comparative analytical study and literature review were conducted using published forensic and computer science research articles from indexed databases. Various AI-based facial recognition algorithms, including deep learning and convolutional neural network (CNN) models, were assessed based on recognition accuracy, processing speed, false positive rates, and forensic applicability.
Results: Deep learning-based facial recognition systems demonstrated significantly higher identification accuracy (95–99%) compared to traditional biometric methods. CNN-based algorithms showed improved performance in low-quality surveillance images and real-time identification tasks. However, challenges including racial bias, dataset limitations, privacy concerns, and susceptibility to image manipulation were identified.
Conclusion: Artificial intelligence has substantially improved the efficiency and accuracy of forensic facial recognition systems. Nevertheless, ethical concerns, algorithmic bias, and legal considerations require further attention to ensure reliable and fair forensic applications.
Keywords: Artificial intelligence, Facial recognition, Forensic science, Deep learning, Convolutional neural networks, Biometrics