  Camera-based physiological measurement is a growing field with neural models
providing state-the-art-performance. Prior research have explored various
"end-to-end" models; however these methods still require several preprocessing
steps. These additional operations are often non-trivial to implement making
replication and deployment difficult and can even have a higher computational
budget than the "core" network itself. In this paper, we propose two novel and
efficient neural models for camera-based physiological measurement called
EfficientPhys that remove the need for face detection, segmentation,
normalization, color space transformation or any other preprocessing steps.
Using an input of raw video frames, our models achieve strong performance on
three public datasets. We show that this is the case whether using a
transformer or convolutional backbone. We further evaluate the latency of the
proposed networks and show that our most light weight network also achieves a
33% improvement in efficiency.
