Abstract
Due to COVID-19, intelligent thermal imagers are widely used all over the world. Since intelligent thermal imagers usually require real-time temperature measurement, it is significant to find a method to quickly and accurately detect human faces in thermal infrared images. This paper mainly proposes two different methods. One is to use image processing methods and face detected from visible images to determine the position of the face in the infrared image, while the other is to use target detection algorithms on infrared images, including YOLOv3 and Faster R-CNN. This paper uses the two methods on a self-collected dataset containing 944 pairs of visible and infrared images and observes the robustness of methods by adding random noise to images. Experiments show that the first one has much lower latency and the latter one has higher accuracy in both cases.
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Chen, Y., Wang, L., Xu, G. (2022). Face Detection on Thermal Infrared Images Combined with Visible Images. In: Pan, L., Cui, Z., Cai, J., Li, L. (eds) Bio-Inspired Computing: Theories and Applications. BIC-TA 2021. Communications in Computer and Information Science, vol 1566. Springer, Singapore. https://doi.org/10.1007/978-981-19-1253-5_26
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DOI: https://doi.org/10.1007/978-981-19-1253-5_26
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