In today’s interconnected world, ensuring the safety of airports assumes paramount importance, given their pivotal role in facilitating global transportation. The rapid evolution of face recognition technology emerges as a pivotal tool in fortifying airport security measures. This study meticulously examines three prominent convolutional neural network (CNN) models: VGGNET, GoogLeNet, and ResNet, evaluating their effectiveness in recognizing faces across diverse and uncontrolled scenarios, leveraging the extensive LFW dataset. The findings unequivocally demonstrate ResNet’s superiority in unconstrained face recognition scenarios compared to its counterparts. Following rigorous training on the LFW dataset, the ResNet-50 model achieves a remarkable accuracy rate of 71.9%. Consequently, the study infers that the ResNet-50 model exhibits exceptional suitability for deployment within airport environments, offering heightened security protocols. This research underscores the pivotal role of CNN-based face recognition technology in enhancing the robustness of airport security measures, thereby contributing significantly to the safety and efficiency of global transportation hubs.