TYrPPG: Uncomplicated and Enhanced Learning Capability rPPG for Remote Heart Rate Estimation

• URL: http://arxiv.org/abs/2511.05833v1
• Date: Sat, 08 Nov 2025 03:46:58 GMT
• Title: TYrPPG: Uncomplicated and Enhanced Learning Capability rPPG for Remote Heart Rate Estimation
• Authors: Taixi Chen, Yiu-ming Cheung,
• Abstract summary: This paper introduces an innovative video understanding block (GVB) designed for efficient RGB videos.<n>Based on the Mam structure, this block integrates 2D-CNN and 3D-CNN to enhance video understanding for analysis.<n>Experiments show that our TYr can achieve state-of-the-art performance in commonly used datasets.
• Score: 51.56484100374058
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Remote photoplethysmography (rPPG) can remotely extract physiological signals from RGB video, which has many advantages in detecting heart rate, such as low cost and no invasion to patients. The existing rPPG model is usually based on the transformer module, which has low computation efficiency. Recently, the Mamba model has garnered increasing attention due to its efficient performance in natural language processing tasks, demonstrating potential as a substitute for transformer-based algorithms. However, the Mambaout model and its variants prove that the SSM module, which is the core component of the Mamba model, is unnecessary for the vision task. Therefore, we hope to prove the feasibility of using the Mambaout-based module to remotely learn the heart rate. Specifically, we propose a novel rPPG algorithm called uncomplicated and enhanced learning capability rPPG (TYrPPG). This paper introduces an innovative gated video understanding block (GVB) designed for efficient analysis of RGB videos. Based on the Mambaout structure, this block integrates 2D-CNN and 3D-CNN to enhance video understanding for analysis. In addition, we propose a comprehensive supervised loss function (CSL) to improve the model's learning capability, along with its weakly supervised variants. The experiments show that our TYrPPG can achieve state-of-the-art performance in commonly used datasets, indicating its prospects and superiority in remote heart rate estimation. The source code is available at https://github.com/Taixi-CHEN/TYrPPG.

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