In this study, we proposed an AI-algorithm for face mask recognition based on the MobileNetV2 network to implement automatic door control in intensive care units. The proposed network was constructed using four bottleneck blocks, incorporating depth-wise separable convolution with channel expansion/projection to minimize computational costs. The performance of the proposed network was compared with other networks trained with an identical dataset. Our network demonstrated higher accuracy than other networks. It also had less trainable total parameters. Additionally, we employed the CVzone-based machine learning model to automatically detect face location. The neural network for mask recognition and the face detection model were integrated into a system for real-time door control using Arduino. Consequently, the proposed algorithm could automatically verify the wearing of masks upon entry to intensive care units, thereby preventing respiratory disease infections among patients and medical staff. The low computational cost and high accuracy of the proposed algorithm also provide excellent performance for real-time mask recognition in actual environments.