TFW: Annotated Thermal Faces in the Wild Dataset

Abstract

Face detection and subsequent localization of facial landmarks are the primary steps in many face applications. Numerous algorithms and benchmark datasets have been introduced to develop robust models for the visible domain. However, varying conditions of illumination still pose challenging problems. In this regard, thermal cameras are employed to address this problem because they operate on longer wavelengths. However, thermal face and facial landmark detection in the wild is an open research problem because most of the existing thermal datasets were collected in controlled environments. In addition, many of them were not annotated with face bounding boxes and facial landmarks. In this work, we present a thermal face dataset with manually labeled bounding boxes and facial landmarks to address these problems. The dataset contains 9,982 images of 147 subjects, collected under controlled and uncontrolled conditions. As a baseline, we trained YOLOv5 object detection model and its adaptation for face detection, YOLO5Face, on our dataset. In addition to our test set, we evaluated the models on the external RWTH-Aachen thermal face dataset to show the efficacy of our dataset. We have made the dataset, source code, and pre-trained models publicly available at https://github.com/IS2AI/TFW to bolster research in thermal face analysis.<br>

A Novel Fully Annotated Thermal Infrared Face Dataset: Recorded in Various Environment Conditions and Distances From The Camera

“…Occlusion, head position changes, various scales, face masks, diverse settings, and weather conditions are all part of the set. The FLIR T540 thermal camera, with a resolution of 464 x 348 pixels, a waveband of 7.5-14 m, a field of view of 24, and an iron color palette, was used to capture the datasets [82].…”