An overview of thermal face recognition methods

Krišto, Mate; Ivašić-Kos, Marina

doi:10.23919/mipro.2018.8400200

Cited by 47 publications

(17 citation statements)

References 32 publications

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“…Cameras operating in the MWIR and LWIR bands ( Fig. 2) do not require an additional source of light or heat, because the thermal radiation sensors in these ranges capture the emitted thermal energy of observed objects [41,42]. For this reason, unlike visible light cameras, they are invariant to illumination conditions, robust to a wide range of light variations [43,44] and weather conditions, and can operate in total darkness.…”

Section: Thermal Imagerymentioning

confidence: 99%

Thermal Object Detection in Difficult Weather Conditions Using YOLO

2020

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Section: Thermal Imagerymentioning

confidence: 99%

Thermal Object Detection in Difficult Weather Conditions Using YOLO

2020

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“…The used dataset for training the proposed model is collected from several available thermal camera videos for detection of pedestrians approaching or waiting at the crosswalk [16]. Samples of the video frames are shown in Fig.…”

Section: Pedestrian and Patient Detectionmentioning

confidence: 99%

“…Unlike conventional cameras, the thermal camera is robust enough against harmful weather conditions and wider light spectral variations e.g., harmful UV. Coupled with sophisticated algorithms such as Deep Learning, these cameras can function as image recognition systems [16].…”

Section: Introductionmentioning

confidence: 99%

Coronavirus Spread Limitation Using Detective Smart System

Ismail

Galal

Saad

2021

Preprint

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Given the circumstances the world is going through due to the novel coronavirus (Covid-19); this paper proposes a new smart system that aims to reduce the spread of the virus. The proposed Covid-19 containment system is designed to be installed outside hospitals and medical centers. Additionally, it works at night as well as at daylight. The system is based on Deep Learning applied to pedestrian temperature data sets that are collected using thermal cameras. The data set is primarily of temperature of pedestrians around medical centers. The thermal cameras are paired with conventional cameras for image capturing and cross referencing the target pedestrian with an existing central database (Big Data). If target is positive, the system sends a text message to the potentially infected person's cell phone upon recognition. The advisory sent text may contain useful information such as the nearest testing or isolation facility. This proposed system is assumed to be linked with the bigger network of the country’s Covid-19 response efforts. The simulation results reveal that the system can achieve an average precision of 90% fever detection among pedestrians.

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“…A third major research area is face recognition and person identification with thermal images. Notable publications include [28] and the overview given in [29].…”

Section: State Of the Artmentioning

confidence: 99%

A Modular System for Detection, Tracking and Analysis of Human Faces in Thermal Infrared Recordings

Kopaczka

Breuer

Schock

et al. 2019

Sensors

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We present a system that utilizes a range of image processing algorithms to allow fully automated thermal face analysis under both laboratory and real-world conditions. We implement methods for face detection, facial landmark detection, face frontalization and analysis, combining all of these into a fully automated workflow. The system is fully modular and allows implementing own additional algorithms for improved performance or specialized tasks. Our suggested pipeline contains a histogtam of oriented gradients support vector machine (HOG-SVM) based face detector and different landmark detecion methods implemented using feature-based active appearance models, deep alignment networks and a deep shape regression network. Face frontalization is achieved by utilizing piecewise affine transformations. For the final analysis, we present an emotion recognition system that utilizes HOG features and a random forest classifier and a respiratory rate analysis module that computes average temperatures from an automatically detected region of interest. Results show that our combined system achieves a performance which is comparable to current stand-alone state-of-the-art methods for thermal face and landmark datection and a classification accuracy of 65.75% for four basic emotions.

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An overview of thermal face recognition methods

Cited by 47 publications

References 32 publications

Thermal Object Detection in Difficult Weather Conditions Using YOLO

Thermal Object Detection in Difficult Weather Conditions Using YOLO

Coronavirus Spread Limitation Using Detective Smart System

A Modular System for Detection, Tracking and Analysis of Human Faces in Thermal Infrared Recordings

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