Fast Skin Segmentation on Low-Resolution Grayscale Images for Remote PhotoPlethysmoGraphy

Paracchini, Marco; Marcon, Marco; Villa, Federica; Cusini, Iris; Tubaro, Stefano

doi:10.1109/mmul.2022.3152087

Cited by 2 publications

(1 citation statement)

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“…The main challenges still need to be mitigated are the following: An end-to-end model proposed in [85] using undercomplete independent component analysis U-LMA was tested under three scenarios to estimate the nonlinear cumulative density function (CDF). Another skin segmentation method was introduced in [86] to process low-resolution inputs, make use of depth-wise convolutional layers, and localize skin pixels. The authors proved the real-time better performance on a small IoT device.…”

Section: End-to-end Learning-based Approachmentioning

confidence: 99%

Intelligent Remote Photoplethysmography-Based Methods for Heart Rate Estimation from Face Videos: A Survey

Premkumar

Hemanth

2022

Informatics

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Over the last few years, a rich amount of research has been conducted on remote vital sign monitoring of the human body. Remote photoplethysmography (rPPG) is a camera-based, unobtrusive technology that allows continuous monitoring of changes in vital signs and thereby helps to diagnose and treat diseases earlier in an effective manner. Recent advances in computer vision and its extensive applications have led to rPPG being in high demand. This paper specifically presents a survey on different remote photoplethysmography methods and investigates all facets of heart rate analysis. We explore the investigation of the challenges of the video-based rPPG method and extend it to the recent advancements in the literature. We discuss the gap within the literature and suggestions for future directions.

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Section: End-to-end Learning-based Approachmentioning

confidence: 99%