Extraction of the superficial facial vasculature, vital signs waveforms and rates using thermal imaging

Gault, Travis R.; Blumenthal, N R; Farag, Aly A.; Starr, Thomas L.

doi:10.1109/cvprw.2010.5544602

Cited by 20 publications

(7 citation statements)

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“…Direct evidence supports this assertion regarding HFR in the visible and near-infrared spectrums [7,11,12,16]. Although there are few direct HFR experiments using thermal imaging as a primary imaging modality, experiments that involve extracting the heart rate and vasculature [2,4,14,17] from thermal video, focus on the forehead, forearm, or carotid artery areas, due to reasons mentioned previously. Experiments that involve extracting the breathing rate [3], focus naturally on the nose area beneath the nostrils and surrounding tissue, but the temperature variations in the nose area ( 2-10 K) are greater than variations on the skin due to blood vessels ( 200 mK), which demonstrates thermal variability around the nose area of the face [10].…”

Section: B Goal Of This Workmentioning

confidence: 69%

Less is more: Cropping to improve facial recognition with thermal images

Gault

Mostafa

Farag

et al. 2011

2011 International Conference on Multimedia Technology

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Numerous studies have investigated the functionality of the human facial recognition mechanisms with aims to improve computer vision methodologies. Understanding these mechanisms is important for applications in computer vision, as one goal of researchers is to create automatic facial recognition systems to equal the robustness of the human facial recognition system. Work in the field of human facial recognition shows that humans are able to recognize faces under very challenging scenarios, including partial occlusion. However, most human facial recognition experiments are conducted in the visible and near-infrared spectrums. During human facial recognition experiments with thermal images conducted in the lab, we found that removing the lower face yields better recognition results than when using the entire face. These tests included standard facial recognition algorithms and the results showed Rank 1 Recognition improvements of 10-25%, depending on the amount of occlusion derived from certain anatomical landmarks.I.

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Section: B Goal Of This Workmentioning

confidence: 69%

Less is more: Cropping to improve facial recognition with thermal images

Gault

Mostafa

Farag

et al. 2011

2011 International Conference on Multimedia Technology

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“…Thus the reader should understand that we use this term for the sake of consistency with previous work, and that we do not claim that what we extract in this paper is an actual vascular network. Rather we prefer to think of our representation as a function of the underlying vasculature" (the reader may also find the work of Gault et al [64] useful in the consideration of this issue).…”

Section: B Feature-based Methodsmentioning

confidence: 99%

Infrared face recognition: A literature review

Ghiass

Arandjelović

Bendada

et al. 2013

The 2013 International Joint Conference on Neural Networks (IJCNN)

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Abstract-Automatic face recognition (AFR) is an area with immense practical potential which includes a wide range of commercial and law enforcement applications, and it continues to be one of the most active research areas of computer vision. Even after over three decades of intense research, the state-of-the-art in AFR continues to improve, benefiting from advances in a range of different fields including image processing, pattern recognition, computer graphics and physiology. However, systems based on visible spectrum images continue to face challenges in the presence of illumination, pose and expression changes, as well as facial disguises, all of which can significantly decrease their accuracy. Amongst various approaches which have been proposed in an attempt to overcome these limitations, the use of infrared (IR) imaging has emerged as a particularly promising research direction. This paper presents a comprehensive and timely review of the literature on this subject.

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“…Microwave and Doppler radars measure HR and respiration rate by sensing movement of the chest during inspiration and expiration (Droitcour et al, 2001 ; Lohman et al, 2002 ; Uenoyama et al, 2006 ; Fletcher and Han, 2009 ; Li et al, 2009 ; Suzuki et al, 2009 ; Gu et al, 2010 ; Kao et al, 2013 ; Lim et al, 2015 ). Thermal imaging uses temperature variations in the wrist, face (nasal), and neck (carotid artery and jugular vein) caused by blood flow moving from the heart to the brain to monitor HR (Chekmenev et al, 2005 , 2009 ; Sun et al, 2006 ; Garbey et al, 2007 ; Gault et al, 2010 ) and respiration rate (Chekmenev et al, 2005 ; Murthy and Pavlidis, 2005 ; Murthy et al, 2009 ; Fei and Pavlidis, 2010 ; Abbas et al, 2011 ). Web-cams detect color variations in the face, and micro-movements in the head and chest caused by blood flow and expiration-inspiration, and extract information related to cardiac activity and respiration from these factors (Poh et al, 2011 ; Balakrishnan et al, 2013 ; Holton et al, 2013 ; Janssen et al, 2015 ).…”

Section: Introductionmentioning

confidence: 99%

Infrared Camera-Based Non-contact Measurement of Brain Activity From Pupillary Rhythms

Park

Whang

2018

Front. Physiol.

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Pupillary responses are associated with affective processing, cognitive function, perception, memory, attention, and other brain activities involving neural pathways. The present study aimed to develop a noncontact system to measure brain activity based on pupillary rhythms using an infra-red web camera. Electroencephalogram (EEG) signals and pupil imaging of 70 undergraduate volunteers (35 female, 35 male) were measured in response to sound stimuli designed to evoke arousal, relaxation, happiness, sadness, or neutral responses. This study successfully developed a real-time system that could detect an EEG spectral index (relative power: low beta in FP1; mid beta in FP1; SMR in FP1; beta in F3; high beta in F8; gamma P4; mu in C4) from pupillary rhythms using the synchronization phenomenon in harmonic frequency (1/100 f) between the pupil and brain oscillations. This method was effective in measuring and evaluating brain activity using a simple, low-cost, noncontact system, and may be an alternative to previous methods used to evaluate brain activity.

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Extraction of the superficial facial vasculature, vital signs waveforms and rates using thermal imaging

Cited by 20 publications

References 10 publications

Less is more: Cropping to improve facial recognition with thermal images

Less is more: Cropping to improve facial recognition with thermal images

Infrared face recognition: A literature review

Infrared Camera-Based Non-contact Measurement of Brain Activity From Pupillary Rhythms

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