1,028 research outputs found
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
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
Remote Bio-Sensing: Open Source Benchmark Framework for Fair Evaluation of rPPG
Remote Photoplethysmography (rPPG) is a technology that utilizes the light
absorption properties of hemoglobin, captured via camera, to analyze and
measure blood volume pulse (BVP). By analyzing the measured BVP, various
physiological signals such as heart rate, stress levels, and blood pressure can
be derived, enabling applications such as the early prediction of
cardiovascular diseases. rPPG is a rapidly evolving field as it allows the
measurement of vital signals using camera-equipped devices without the need for
additional devices such as blood pressure monitors or pulse oximeters, and
without the assistance of medical experts. Despite extensive efforts and
advances in this field, serious challenges remain, including issues related to
skin color, camera characteristics, ambient lighting, and other sources of
noise, which degrade performance accuracy. We argue that fair and evaluable
benchmarking is urgently required to overcome these challenges and make any
meaningful progress from both academic and commercial perspectives. In most
existing work, models are trained, tested, and validated only on limited
datasets. Worse still, some studies lack available code or reproducibility,
making it difficult to fairly evaluate and compare performance. Therefore, the
purpose of this study is to provide a benchmarking framework to evaluate
various rPPG techniques across a wide range of datasets for fair evaluation and
comparison, including both conventional non-deep neural network (non-DNN) and
deep neural network (DNN) methods. GitHub URL:
https://github.com/remotebiosensing/rppg.Comment: 19 pages, 10 figure
Multi-hierarchical Convolutional Network for Efficient Remote Photoplethysmograph Signal and Heart Rate Estimation from Face Video Clips
Heart beat rhythm and heart rate (HR) are important physiological parameters
of the human body. This study presents an efficient multi-hierarchical
spatio-temporal convolutional network that can quickly estimate remote
physiological (rPPG) signal and HR from face video clips. First, the facial
color distribution characteristics are extracted using a low-level face feature
Generation (LFFG) module. Then, the three-dimensional (3D) spatio-temporal
stack convolution module (STSC) and multi-hierarchical feature fusion module
(MHFF) are used to strengthen the spatio-temporal correlation of multi-channel
features. In the MHFF, sparse optical flow is used to capture the tiny motion
information of faces between frames and generate a self-adaptive region of
interest (ROI) skin mask. Finally, the signal prediction module (SP) is used to
extract the estimated rPPG signal. The experimental results on the three
datasets show that the proposed network outperforms the state-of-the-art
methods.Comment: 33 pages,9 figure
- …