  Architectural improvements are studied for convolutional network performing
estimation of heart rate (HR) values on color signal patches. Color signals are
time series of color components averaged over facial regions recorded by
webcams in two scenarios: Stationary (without motion of a person) and Mixed
Motion (different motion patterns of a person). HR estimation problem is
addressed as a classification task, where classes correspond to different heart
rate values within the admissible range of [40; 125] bpm. Both adding
convolutional filtering layers after fully connected layers and involving
combined loss function where first component is a cross entropy and second is a
squared error between the network output and smoothed one-hot vector, lead to
better performance of HR estimation model in Stationary and Mixed Motion
scenarios.
