Omkar2118/datasets huggingface dataset:
TFW: Annotated Thermal Faces in the Wild Dataset
Full name: TFW: Annotated Thermal Faces in the Wild Dataset

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 the 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.
SF-TL54: A Thermal Facial Landmark Dataset with Visual Pairs
Full name: SF-TL54: A Thermal Facial Landmark Dataset with Visual Pairs

Facial landmark detection is a cornerstone in many facial analysis tasks such as face recognition, drowsiness detection, and facial expression recognition. Numerous methodologies were introduced to achieve accurate and efficient facial landmark localization in visual images. However, there are only several works that address facial landmark detection in thermal images. The main challenge is the limited number of annotated datasets. In this work, we present a thermal face dataset with annotated face bounding boxes and facial landmarks. The dataset contains 2,556 thermal images of 142 individuals, where each thermal image is paired with the corresponding visual image. To the best of our knowledge, our dataset is the largest in terms of the number of individuals. In addition, our dataset can be employed for tasks such as thermal-to-visual image translation, thermal-visual face recognition, and others. We trained two models for the facial landmark detection task to show the efficacy of our dataset. The first model is a classic machine learning model based on an ensemble of regression trees. The second model is a deep learning model based on the U-net architecture. The dataset, annotations, source code, and pre-trained models are publicly available to advance research in thermal face analysis.