FACIAL RECOGNITION SYSTEM USING A DEEP LEARNING APPROACH: A REVIEW
Keywords:
Facial Recognition, Biometric Systems, Deep Learning, Convolutional Neural Networks (CNNs), Deep Metric LearningAbstract
Facial recognition has emerged as a leading biometric technology, with deep learning driving significant improvements in accuracy, robustness, and applicability. This study reviews the progression from early handcrafted feature extraction methods to advanced neural architectures such as convolutional neural networks (CNNs), deep metric learning, attention mechanisms, and hybrid CNN–Transformer models. It highlights how deep learning enables automatic hierarchical feature learning, improving resilience against variations in pose, illumination, occlusion, and facial expressions. The review also explores diverse applications, including identity verification, emotion recognition, healthcare diagnostics, attendance monitoring, surveillance, and DeepFake detection, underscoring the interdisciplinary relevance of facial recognition systems. Despite these advances, challenges such as dataset bias, privacy concerns, computational demands, and vulnerability to adversarial attacks persist. Emerging solutions which includes multimodal biometric integration, liveness detection, federated learning, and lightweight architectures for mobile and edge devices are discussed as pathways toward more secure and efficient systems. The study concludes that while deep learning has elevated facial recognition to unprecedented levels of performance, sustainable adoption requires balancing technical innovation with ethical governance, fairness, and privacy preservation.
