EfficientPhys: Enabling Simple, Fast and Accurate Camera-Based Vitals Measurement

• URL: http://arxiv.org/abs/2110.04447v1
• Date: Sat, 9 Oct 2021 03:51:26 GMT
• Title: EfficientPhys: Enabling Simple, Fast and Accurate Camera-Based Vitals Measurement
• Authors: Xin Liu, Brian L. Hill, Ziheng Jiang, Shwetak Patel, Daniel McDuff
• Abstract summary: We propose two novel neural models for camera-based physiological measurement called EfficientPhys. Our models achieve state-of-the-art accuracy on three public datasets.
• Score: 5.435325323159416
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 state-of-the-art accuracy 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.

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