Facial skin segmentation is an important preliminary task in many applications, including remote PhotoPlethysmoGraphy (rPPG), which is the problem of estimating the heart activity of a subject just by analysing a video of their face. By selecting all the subjects skin surface, a more robust pulse signal could be extracted and analyzed in order to provide an accurate heart activity monitoring. Single-Photon Avalanche Diode (SPAD) cameras have proven to be able to achieve better results in rPPG than traditional cameras. Altought this kind of cameras produces accurate photon counts at high frame rate they are able to capture just grayscale low resolution images. For this reason, in this work, we propose a novel skin segmentation method based on deep learning that is able to precisely localize skin pixels inside a low resolution grayscale image. Moreover since the proposed method makes use of depthwise separable convolutional layers it could achieve real time performances even when implemented on a small low powered IoT device.

Fast Skin Segmentation on Low Resolution Grayscale Images for Remote PhotoPlethysmoGraphy

Marco Brando Mario Paracchini;Marco Marcon;Federica Villa;Iris Cusini;Stefano Tubaro
2022-01-01

Abstract

Facial skin segmentation is an important preliminary task in many applications, including remote PhotoPlethysmoGraphy (rPPG), which is the problem of estimating the heart activity of a subject just by analysing a video of their face. By selecting all the subjects skin surface, a more robust pulse signal could be extracted and analyzed in order to provide an accurate heart activity monitoring. Single-Photon Avalanche Diode (SPAD) cameras have proven to be able to achieve better results in rPPG than traditional cameras. Altought this kind of cameras produces accurate photon counts at high frame rate they are able to capture just grayscale low resolution images. For this reason, in this work, we propose a novel skin segmentation method based on deep learning that is able to precisely localize skin pixels inside a low resolution grayscale image. Moreover since the proposed method makes use of depthwise separable convolutional layers it could achieve real time performances even when implemented on a small low powered IoT device.
2022
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1233084
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