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Architectural improvements are studied for convolutional network performingestimation of heart rate (HR) values on color signal patches. Color signals aretime series of color components averaged over facial regions recorded bywebcams in two scenarios: Stationary (without motion of a person) and MixedMotion (different motion patterns of a person). HR estimation problem isaddressed as a classification task, where classes correspond to different heartrate values within the admissible range of [40; 125] bpm. Both addingconvolutional filtering layers after fully connected layers and involvingcombined loss function where first component is a cross entropy and second is asquared error between the network output and smoothed one-hot vector, lead tobetter performance of HR estimation model in Stationary and Mixed Motionscenarios.

Architectural Tricks for Deep Learning in Remote Photoplethysmography