Facial Recognition System

Facial recognition systems aim to automatically identify individuals from their facial images, with applications ranging from security to healthcare. Current research heavily focuses on mitigating biases (e.g., racial, gender) in these systems, often employing novel metric learning techniques and contrastive learning strategies to improve fairness and accuracy across diverse demographics. Furthermore, significant effort is dedicated to enhancing robustness against low-resolution images, spoofing attacks (both physical and digital), and adversarial examples, often leveraging deep learning models like GANs and autoencoders, along with innovative approaches like patch-based analysis and frame skipping. These advancements are crucial for ensuring the ethical and reliable deployment of facial recognition technology in various real-world applications.