Facial Recognition Algorithms: A Systematic Literature Review
1Department of Computer Engineering, College of Computing, Fahad Bin Sultan University, Tabuk 71454, Saudi Arabia.
Facial recognition technology has advanced significantly with deep learning, but faces challenges in privacy, ethics, and bias. Responsible development requires ethical guidelines and further research to ensure reliability and user trust.
Area of Science:
- Computer Science
- Artificial Intelligence
- Biometrics
Background:
- Facial recognition technology (FRT) has evolved significantly, impacting various sectors.
- Understanding its principles, metrics, and applications is crucial.
Purpose of the Study:
- To review new developments and challenges in facial recognition technology.
- To explore algorithm techniques, performance metrics, and applications in health, society, and security.
Main Methods:
- Systematic literature review of academic publications, conferences, and industry news.
- Emphasis on deep learning techniques, particularly Convolutional Neural Networks (CNNs).
Main Results:
- Deep learning, especially CNNs, has dramatically improved FRT accuracy and efficiency.
- Key challenges identified include privacy concerns, ethical dilemmas, and system biases.
Conclusions:
- FRT has evolved, but ethical and regulated use is imperative.
- Future research should focus on reliability, bias reduction, and building user confidence.
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