Multi-spectral Facial Landmark Detection

Keong, Jin; Dong, Xingbo; Jin, Zhe; Mallat, Khawla; Dugelay, Jean‐Luc

doi:10.1109/wifs49906.2020.9360890

Cited by 11 publications

(8 citation statements)

References 17 publications

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“…However, it is possible to automatically annotate the thermal images by applying the state-of-the-art face detection and facial landmark prediction models to their visual pairs. Nonetheless, the automatically detected bounding boxes and facial landmarks might require additional manual calibration [35].…”

Section: Related Workmentioning

confidence: 99%

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TFW: Annotated Thermal Faces in the Wild Dataset

Kuzdeuov¹,

Aubakirova²,

Koishigarina³

et al. 2022

Preprint

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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 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>

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Section: Related Workmentioning

confidence: 99%

“…In addition, facial landmarks are used for face alignment in face recognition. A few works have addressed the training of thermal facial landmark detection models [33]- [35]. In these works, the models were trained on a limited number of thermal images collected in controlled conditions.…”

Section: Introductionmentioning

confidence: 99%

TFW: Annotated Thermal Faces in the Wild Dataset

Kuzdeuov¹,

Aubakirova²,

Koishigarina³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…However, it is possible to automatically annotate the thermal images by applying the state-of-the-art face detection and facial landmark prediction models to their visual pairs. Nonetheless, this process may require additional manual calibration [35].…”

Section: Related Workmentioning

confidence: 99%

TFW: Annotated Thermal Faces in the Wild Dataset

Kuzdeuov¹,

Aubakirova²,

Koishigarina³

et al. 2021

Preprint

View full text Add to dashboard Cite

Face detection and localization of facial landmarks are the primary steps in building many face applications in computer vision. Numerous algorithms and benchmark datasets have been proposed to develop accurate face and facial landmark detection models in the visual domain. However, varying illumination conditions still pose challenging problems. Thermal cameras can address this problem because of their operation in longer wavelengths. However, thermal face detection and localization of facial landmarks in the wild condition are overlooked. The main reason is that most of the existing thermal face datasets have been collected in controlled environments. In addition, many of them contain no annotations of 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 9,202 images of 145 subjects, collected in both controlled and wild conditions. As a baseline, we trained the YOLOv5 object detection model and its adaptation for face detection, YOLO5Face, on our dataset. To show the efficacy of our dataset, we evaluated these models on the RWTH-Aachen thermal face dataset in addition to our test set. We have made the dataset, source code, and pretrained models publicly available at https://github.com/IS2AI/TFW to bolster research in thermal face analysis. <br>

show abstract

“…In addition, facial landmarks are used for face alignment in face recognition. A few works have addressed the training of thermal facial landmark detection models [19]- [21]. In these works, the models were trained on a limited number of thermal images collected in controlled conditions.…”

Section: Introductionmentioning

confidence: 99%

TFW: Annotated Thermal Faces in the Wild Dataset

Kuzdeuov

Aubakirova

Koishigarina

et al. 2022

IEEE Trans.Inform.Forensic Secur.

View full text Add to dashboard Cite

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.

show abstract

Multi-spectral Facial Landmark Detection

Cited by 11 publications

References 17 publications

TFW: Annotated Thermal Faces in the Wild Dataset

TFW: Annotated Thermal Faces in the Wild Dataset

TFW: Annotated Thermal Faces in the Wild Dataset

TFW: Annotated Thermal Faces in the Wild Dataset

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