This paper presents a high-speed and robust dual-band infrared thermal camera based on an ARM CPU. The system consists of a low-resolution long-wavelength infrared detector， a digital temperature and humidity sensor， and a CMOS sensor. In view of the significant contrast between face and background in thermal infrared images， this paper explores a suitable accuracy-latency tradeoff for thermal face detection and proposes a tiny， lightweight detector named YOLO-Fastest-IR. Four YOLO-Fastest-IR models （IR0 to IR3） with different scales are designed based on YOLO-Fastest. To train and evaluate these lightweight models， a multi-user low-resolution thermal face database （RGBT-MLTF） was collected， and the four networks were trained. Experiments demonstrate that the lightweight convolutional neural network performs well in thermal infrared face detection tasks. The proposed algorithm outperforms existing face detection methods in both positioning accuracy and speed， making it more suitable for deployment on mobile platforms or embedded devices. After obtaining the region of interest （ROI） in the infrared （IR） image， the RGB camera is guided by the thermal infrared face detection results to achieve fine positioning of the RGB face. Experimental results show that YOLO-Fastest-IR achieves a frame rate of 92.9 FPS on a Raspberry Pi 4B and successfully detects 97.4% of faces in the RGBT-MLTF test set. Ultimately， an infrared temperature measurement system with low cost， strong robustness， and high real-time performance was realized， achieving a temperature measurement accuracy of 0.3°C.