Toronto NeuroFace Dataset: A New Dataset for Facial Motion Analysis in Individuals with Neurological Disorders Toronto NeuroFace Dataset is a public dataset with videos of oro-facial gestures performed
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Drone Surveillance of Faces, is a large-scale drone dataset intended to facilitate research for face recognition using drones.
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The proposed Extended-YouTube Faces (E-YTF) is an extension of the famous YouTube Faces (YTF) dataset and is specifically designed to further push the challenges of face recognition by addressing the problem of open-set face identification from heterogeneous data i.e. still images vs video.
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…The segments are of varying length, between 3 and 10 seconds long, and in each clip the only visible face in the video and audible sound in the soundtrack belong to a single speaking person. In total, the dataset contains roughly 4700 hours of video segments with approximately 150,000 distinct speakers, spanning a wide variety of people, languages and face poses.
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…The images inside each zip file are face-only. We provide three convenient sizes: 224 x 224, 512 x 512, and 1024 x 1024 pixels. The participant covers their eyes with a hand, followed by covering the left half, the right half, and finally, the lower half of the face. The participant moves a green cloth in front of their face The participant puts on a face mask and counts from 1 to 10 out loud. Then, they remove the facemask. FSGAN (Face Swapping Generative Adversarial Network): This corresponds to the second version of FSGAN. Access its release at https://github.com/wyhsirius/LIA As a rule of thumb, An imposter outer face and target is the inner face, in case of faceswaps.
…It can be used for diverse research fields like visual speech recognition, face detection, and biometrics.
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…contains AR glasses egocentric multi-channel microphone array audio, wide field-of-view RGB video, speech source pose, headset microphone audio, annotated voice activity, speech transcriptions, head and face
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…The WMCA database is produced at Idiap within the framework of “IARPA BATL” and “H2020 TESLA” projects and it is intended for investigation of presentation attack detection (PAD) methods for face recognition
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…Rich annotations and diversity: Each data point is accompanied by detailed metadata (sensor details, weather conditions) and manual annotations (bounding boxes, yaw/pitch angles) for face and whole-body
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…(Data collection was in accordance with IRB protocols and subject faces have been blurred for subject privacy.)
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…The pain stimulation experiment was conducted twice: once with un-occluded face and once with facial EMG sensors.
…The ground truth consists of annotations of human faces and whether they are masks or not.
The dataset is designed specifically to solve a range of computer vision problems (2D-3D tracking, posture) faced by biologists while designing behavior studies with animals.