Eyes' corners detection in infrared images for real-time noncontact respiration rate monitoring

Alkali, Abdulkadir Hamidu; Saatchi, Reza; Elphick, Heather; Burke, Derek

doi:10.1109/wccais.2014.6916570

Cited by 7 publications

(4 citation statements)

References 12 publications

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“…The respiration is then monitored by observing the nostril area. The temperature at that area will change for each time the subject is inhaling and exhaling (Alkali et al, 2014).…”

Section: Eyes' Corners Detection In Infrared Images For Real-time Non...mentioning

confidence: 99%

Using Facial Landmark Detection on Thermal Images as a Novel Prognostic Tool for Emergency Departments

Baskaran

Møller

Wiil

et al. 2022

Front. Artif. Intell.

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IntroductionEmergency departments (ED) at hospitals sometimes experience unexpected deterioration in patients that were assessed to be in a stable condition upon arrival. Odense University Hospital (OUH) has conducted a retrospective study to investigate the possibilities of prognostic tools that can detect these unexpected deterioration cases at an earlier stage. The study suggests that the temperature difference (gradient) between the core and the peripheral body parts can be used to detect these cases. The temperature between the patient's inner canthus (core temperature) and the tip of the nose (peripheral temperature) can be measured with a thermal camera. Based on the temperature measurement from a thermal image, a gradient value can be calculated, which can be used as an early indicator of potential deterioration.ProblemThe lack of a tool to automatically calculate the gradient has prevented the ED at OUH in conducting a comprehensive prospective study on early indicators of patients at risk of deterioration. The current manual way of doing facial landmark detection on thermal images is too time consuming and not feasible as part of the daily workflow at the ED, where nurses have to triage patients within a few minutes.ObjectiveThe objective of this study was to automate the process of calculating the gradient by developing a handheld prognostic tool that can be used by nurses for automatically performing facial landmark detection on thermal images of patients as they arrive at the ED.MethodsA systematic literature review has been conducted to investigate previous studies that have been done for applying computer vision methods on thermal images. Several meetings, interviews and field studies have been conducted with the ED at OUH in order to understand their workflow, formulate and prioritize requirements and co-design the prognostic tool.ResultsThe study resulted in a novel Android app that can capture a thermal image of a patient's face with a thermal camera attached to a smartphone. Within a few seconds, the app then automatically calculates the gradient to be used in the triage process. The developed tool is the first of its kind using facial landmark detection on thermal images for calculating a gradient that can serve as a novel prognostic indicator for ED patients.

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“…The respiration is then monitored by observing the nostril area. The temperature at that area will change for each time the subject is inhaling and exhaling (Alkali et al, 2014).…”

Section: Eyes' Corners Detection In Infrared Images For Real-time Non...mentioning

confidence: 99%

Using Facial Landmark Detection on Thermal Images as a Novel Prognostic Tool for Emergency Departments

Baskaran

Møller

Wiil

et al. 2022

Front. Artif. Intell.

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“…Wang et al [31] trained a SVM to perform binary classification of the eye region based on Haar-like features. Alkali et al [1] located the temperature maxima as it is commonly situated in the inner corner of the eyes.…”

Section: Related Workmentioning

confidence: 99%

Facial landmark detection on thermal data via fully annotated visible-to-thermal data synthesis

Mallat¹,

Dugelay²

2020

2020 IEEE International Joint Conference on Biometrics (IJCB)

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Thermal imaging has substantially evolved, during the recent years, to be established as a complement, or even occasionally as an alternative to conventional visible light imaging, particularly for face analysis applications. Facial landmark detection is a crucial prerequisite for facial image processing. Given the upswing of deep learning based approaches, the performance of facial landmark detection has been significantly improved. However, this uprise is merely limited to visible spectrum based face analysis tasks, as there are only few research works on facial landmark detection in thermal spectrum. This limitation is mainly due to the lack of available thermal face databases provided with full facial landmark annotations. In this paper, we propose to tackle this data shortage by converting existing face databases, designed for facial landmark detection task, from visible to thermal spectrum that will share the same provided facial landmark annotations. Using the synthesized thermal databases along with the facial landmark annotations, two different models are trained using active appearance models and deep alignment network. Evaluating the models trained on synthesized thermal data on real thermal data, we obtained facial landmark detection accuracy of 94.59% when tested on low quality thermal data and 95.63% when tested on high quality thermal data with a detection threshold of 0.15×IOD.

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“…More advanced approaches employ a basic segmentation and add a feature detection step to the process. Current algorithms often perform landmark detection by locating temperature maxima which can usually be found in the inner corner of the eyes (Alkali et al, 2014). These methods generally assume a frontal view of the face and their performance degrades quickly when confronted with out-of-plane rotation.…”

Section: Previous Workmentioning

confidence: 99%

Robust Facial Landmark Detection and Face Tracking in Thermal Infrared Images using Active Appearance Models

Kopaczka

Acar

Merhof

2016

Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications

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Long wave infrared (LWIR) imaging is an imaging modality currently gaining increasing attention. Facial images acquired with LWIR sensors can be used for illumination invariant person recognition and the contactless extraction of vital signs such as respiratory rate. In order to work properly, these applications require a precise detection of faces and regions of interest such as eyes or nose. Most current facial landmark detectors in the LWIR spectrum localize single salient facial regions by thresholding. These approaches are not robust against out-of-plane rotation and occlusion. To address this problem, we therefore introduce a LWIR face tracking method based on an active appearance model (AAM). The model is trained with a manually annotated database of thermal face images. Additionally, we evaluate the effect of different methods for AAM generation and image preprocessing on the fitting performance. The method is evaluated on a set of still images and a video sequence. Results show that AAMs are a robust method for the detection and tracking of facial landmarks in the LWIR spectrum.

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Eyes' corners detection in infrared images for real-time noncontact respiration rate monitoring

Cited by 7 publications

References 12 publications

Using Facial Landmark Detection on Thermal Images as a Novel Prognostic Tool for Emergency Departments

Using Facial Landmark Detection on Thermal Images as a Novel Prognostic Tool for Emergency Departments

Facial landmark detection on thermal data via fully annotated visible-to-thermal data synthesis

Robust Facial Landmark Detection and Face Tracking in Thermal Infrared Images using Active Appearance Models

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