Border Control

Border control research encompasses diverse areas, from enhancing security systems to improving the efficiency and fairness of processes. Current efforts focus on developing robust computer vision models, often employing convolutional neural networks (CNNs), to detect threats and verify identities, particularly addressing challenges like morphing attacks and image resolution variations. These advancements aim to improve the accuracy and reliability of automated border control systems while also exploring methods to mitigate biases and ensure fairness in decision-making. The ultimate goal is to create more secure and efficient border management systems while addressing ethical considerations.