Abstract: A method performed by at least one processor includes obtaining an image of a subject; preprocessing the image of the subject; inputting the preprocessed image into a machine learning model trained in accordance with a first frequency distribution corresponding to a first ground truth obtained from one or more sensors performing a vital measurement on one or more test subjects; and obtaining, from the machine learning model, an estimate of a signal corresponding to the vital measurement of the subject.

Abstract: In one embodiment, a method includes accessing a video of a user's face. The method further includes accessing, for each image frame in the video, (1) one or more facial landmarks determined by a facial landmark detection (FLD) model and (2) a corresponding determined position in the image for each facial landmark. The method further includes determining, based on the one or more facial landmarks and corresponding positions, a motion of the user's face in the captured video; extracting, from the determined motion of the user's face, a corrected motion signal of the user's face; adjusting, based on the extracted corrected motion signal of the user's face, the positions of one or more facial landmarks in the image frames; and determining, based at least in part on the adjusted positions of the facial landmarks in the sequential images of the video, one or more vital signs of the user.

Abstract: Liveness detection includes systems, devices, methods, and non-transitory instructions for detecting liveness of a subject from a media stream, including capturing a media stream of the subject, the media stream including a sequence of frames, processing each frame of the media stream to track one or more biometrics, and determining whether the subject in the media stream is live based on the one or more biometrics detected in the media steam.

Abstract: Systems, devices, methods, and non-transitory computer-readable instructions for non-contrastive unsupervised learning of a physiological signal from a video stream, the computer-implemented method including capturing the video stream of a subject, the video stream including a sequence of frames; processing each frame of the video stream to update a physiological signal detection function; determining the physiological signal from the video stream, and applying the updated physiological signal detection function to a subsequent video steam.

Abstract: Systems, devices, methods, and non-transitory computer-readable instructions for detecting deception of a subject from a media stream that capture a media stream of the subject including, the media stream including a sequence of frames, process each frame of the media stream to track a plurality of biometrics, and determine whether the subject in the media stream is deceptive based upon changes to respective biometrics.

Abstract: The video based detection of pulse waveform includes systems, devices, methods, and computer-readable instructions for capturing a video stream including a sequence of frames, processing each frame of the video stream to spatially locate a region of interest, cropping each frame of the video stream to encapsulate the region of interest, processing the sequence of frames, by a 3-dimensional convolutional neural network, to determine the spatial and temporal dimensions of each frame of the sequence of frames and to produce a pulse waveform point for each frame of the sequence of frames, and generating a time series of pulse waveform points to generate the pulse waveform of the subject for the sequence of frames.