A Comparative Evaluation of Heart Rate Estimation Methods using Face Videos

This paper presents a comparative evaluation of methods for remote heart rate estimation using face videos, i.e., given a video sequence of the face as input, methods to process it to obtain a robust estimation of the subjects heart rate at each moment. Four alternatives from the literature are tested, three based in hand crafted approaches and one based on deep learning. The methods are compared using RGB videos from the COHFACE database. Experiments show that the learning-based method achieves much better accuracy than the hand crafted ones. The low error rate achieved by the learning based model makes possible its application in real scenarios, e.g. in medical or sports environments.Comment: Accepted in "IEEE International Workshop on Medical Computing (MediComp) 2020

Protocol of the SOMNIA project : an observational study to create a neurophysiological database for advanced clinical sleep monitoring

Introduction Polysomnography (PSG) is the primary tool for sleep monitoring and the diagnosis of sleep disorders. Recent advances in signal analysis make it possible to reveal more information from this rich data source. Furthermore, many innovative sleep monitoring techniques are being developed that are less obtrusive, easier to use over long time periods and in the home situation. Here, we describe the methods of the Sleep and Obstructive Sleep Apnoea Monitoring with Non-Invasive Applications (SOMNIA) project, yielding a database combining clinical PSG with advanced unobtrusive sleep monitoring modalities in a large cohort of patients with various sleep disorders. The SOMNIA database will facilitate the validation and assessment of the diagnostic value of the new techniques, as well as the development of additional indices and biomarkers derived from new and/or traditional sleep monitoring methods. Methods and analysis We aim to include at least 2100 subjects (both adults and children) with a variety of sleep disorders who undergo a PSG as part of standard clinical care in a dedicated sleep centre. Full-video PSG will be performed according to the standards of the American Academy of Sleep Medicine. Each recording will be supplemented with one or more new monitoring systems, including wrist-worn photoplethysmography and actigraphy, pressure sensing mattresses, multimicrophone recording of respiratory sounds including snoring, suprasternal pressure monitoring and multielectrode electromyography of the diaphragm

A Reproducible Study on Remote Heart Rate Measurement

This paper studies the problem of reproducible research in remote photoplethysmography (rPPG). Most of the work published in this domain is assessed on privately-owned databases, making it difficult to evaluate proposed algorithms in a standard and principled manner. As a consequence, we present a new, publicly available database containing a relatively large number of subjects recorded under two different lighting conditions. Also, three state-of-the-art rPPG algorithms from the literature were selected, implemented and released as open source free software. After a thorough, unbiased experimental evaluation in various settings, it is shown that none of the selected algorithms is precise enough to be used in a real-world scenario

Exploring remote photoplethysmography signals for deepfake detection in facial videos

Abstract. With the advent of deep learning-based facial forgeries, also called "deepfakes", the feld of accurately detecting forged videos has become a quickly growing area of research. For this endeavor, remote photoplethysmography, the process of extracting biological signals such as the blood volume pulse and heart rate from facial videos, offers an interesting avenue for detecting fake videos that appear utterly authentic to the human eye. This thesis presents an end-to-end system for deepfake video classifcation using remote photoplethysmography. The minuscule facial pixel colour changes are used to extract the rPPG signal, from which various features are extracted and used to train an XGBoost classifer. The classifer is then tested using various colour-to-blood volume pulse methods (OMIT, POS, LGI and CHROM) and three feature extraction window lengths of two, four and eight seconds. The classifer was found effective at detecting deepfake videos with an accuracy of 85 %, with minimal performance difference found between the window lengths. The GREEN channel signal was found to be important for this classifcationEtäfotoplethysmografian hyödyntäminen syväväärennösten tunnistamiseen. Tiivistelmä. Syväväärennösten eli syväoppimiseen perustuvien kasvoväärennöksien yleistyessä väärennösten tarkasta tunnistamisesta koneellisesti on tullut nopeasti kasvava tutkimusalue. Etäfotoplethysmografa (rPPG) eli biologisten signaalien kuten veritilavuuspulssin tai sykkeen mittaaminen videokuvasta tarjoaa kiinnostavan keinon tunnistaa väärennöksiä, jotka vaikuttavat täysin aidoilta ihmissilmälle. Tässä diplomityössä esitellään etäfotoplethysmografaan perustuva syväväärennösten tunnistusmetodi. Kasvojen minimaalisia värimuutoksia hyväksikäyttämällä mitataan fotoplethysmografasignaali, josta lasketuilla ominaisuuksilla koulutetaan XGBoost-luokittelija. Luokittelijaa testataan usealla eri värisignaalista veritilavuussignaaliksi muuntavalla metodilla sekä kolmella eri ominaisuuksien ikkunapituudella. Luokittelija pystyy tunnistamaan väärennetyn videon aidosta 85 % tarkkuudella. Eri ikkunapituuksien välillä oli minimaalisia eroja, ja vihreän värin signaalin havaittiin olevan luokittelun suorituskyvyn kannalta merkittävä