ToTMNet: FFT-Accelerated Toeplitz Temporal Mixing Network for Lightweight Remote Photoplethysmography
Vladimir Frants, Sos Agaian, Karen Panetta

TL;DR
ToTMNet is a lightweight, FFT-accelerated neural network for remote photoplethysmography that efficiently models long-range temporal dependencies with fewer parameters than attention-based methods.
Contribution
It introduces a novel Toeplitz-based temporal mixing layer integrated into a compact network, replacing costly attention mechanisms for improved efficiency in rPPG estimation.
Findings
Achieves 1.055 bpm MAE on UBFC-rPPG dataset
Reaches 1.582 bpm MAE in cross-dataset evaluation
Uses only 63k parameters, demonstrating efficiency
Abstract
Remote photoplethysmography (rPPG) estimates a blood volume pulse (BVP) waveform from facial videos captured by commodity cameras. Although recent deep models improve robustness compared to classical signal-processing approaches, many methods increase computational cost and parameter count, and attention-based temporal modeling introduces quadratic scaling with respect to the temporal length. This paper proposes ToTMNet, a lightweight rPPG architecture that replaces temporal attention with an FFT-accelerated Toeplitz temporal mixing layer. The Toeplitz operator provides full-sequence temporal receptive field using a linear number of parameters in the clip length and can be applied in near-linear time using circulant embedding and FFT-based convolution. ToTMNet integrates the global Toeplitz temporal operator into a compact gated temporal mixer that combines a local depthwise temporal…
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Taxonomy
TopicsNon-Invasive Vital Sign Monitoring · Optical Imaging and Spectroscopy Techniques · Heart Rate Variability and Autonomic Control
