  Automatically detecting vital signs in videos, such as the estimation of
heart and respiration rates, is a challenging research problem in computer
vision with important applications in the medical field. One of the key
difficulties in tackling this task is the lack of sufficient supervised
training data, which severely limits the use of powerful deep neural networks.
In this paper we address this limitation through a novel deep learning
approach, in which a recurrent deep neural network is trained to detect vital
signs in the infrared thermal domain from purely synthetic data. What is most
surprising is that our novel method for synthetic training data generation is
general, relatively simple and uses almost no prior medical domain knowledge.
Moreover, our system, which is trained in a purely automatic manner and needs
no human annotation, also learns to predict the respiration or heart intensity
signal for each moment in time and to detect the region of interest that is
most relevant for the given task, e.g. the nose area in the case of
respiration. We test the effectiveness of our proposed system on the recent
LCAS dataset and obtain state-of-the-art results.
