In order to improve the accuracy of face detection of infrared thermal imaging cameras, a face detection method based on dual-band image fusion is proposed, which can perform face detection through Yolo-FastestV2 lightweight convolutional neural network after linear fusion of visible light (RGB) images and infrared (IR) images. Compared with the traditional infrared temperature measurement system that requires separate face detection for infrared and visible images, the dual-band face detection method proposed in this paper requires only one detection to obtain the face position in both IR and RGB images, and reduces the mapping error introduced by the traditional method due to the distance change in the mapping process in the coordinate mapping stage. In order to complete the training and testing of dual-band fusion images, a dual-band image dataset containing visible light and infrared, and the dataset is shot by a dual-band camera, which consists of a visible light detector and an infrared detector, and the two sensors can simultaneously capture RGB images and IR images. The experimental results show that 94.35% of the face images in the test set can be correctly detected using dual-band fusion, and the highest detection frame rate can reach 317 FPS.