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
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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 adap
Toronto NeuroFace Dataset: A New Dataset for Facial Motion Analysis in Individuals with Neurological Disorders