Convolutional Neural Networks for Semantic Segmentation as a Tool for Multiclass Face Analysis in Thermal Infrared

Müller, David; Ehlen, A.; Valeske, Bernd

doi:10.1007/s10921-020-00740-y

Cited by 20 publications

(8 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Sixtyeight facial landmarks were identified in each frame in the visible domain through the open-source software OpenFace [40] and transformed into the corresponding frame of infrared images, allowing to identify anatomical landmarks over the IRI image of the faces with an RMSE of 0.66 pixels. Müller et al [41] employed convolutional neural networks for multiclass segmentation in thermal infrared face analysis. The principle is based on existing image-to-image translation approaches, where each pixel in an image is assigned to a class label.…”

Section: Inner Canthi Identification and Face Segmentation Algorithmsmentioning

confidence: 99%

An Overview of Thermal Infrared Imaging-Based Screenings during Pandemic Emergencies

Perpetuini

Filippini

Cardone

et al. 2021

IJERPH

View full text Add to dashboard Cite

Infrared thermal imaging (IRI) is a contact-less technology able to monitor human skin temperature for biomedical applications and in real-life contexts. Its capacity to detect fever was exploited for mass screening during past epidemic emergencies as well as for the current COVID-19 pandemic. However, the only assessment of fever may not be selective for the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection. Hence, novel approaches for IRI data analysis have been investigated. The present review aims to describe how IRI have been employed during the last epidemics, highlighting the potentialities and the limitations of this technology to contain the contagions. Specifically, the methods employed for automatic face recognition and fever assessment and IRI’s performances in mass screening at airports and hospitals are reviewed. Moreover, an overview of novel machine learning methods for IRI data analysis, aimed to identify respiratory diseases, is provided. In addition, IRI-based smart technologies developed to support the healthcare during the COVID-19 pandemic are described. Finally, relevant guidelines to fully exploit IRI for COVID-19 identification are defined, to improve the effectiveness of IRI in the detection of the SARS-CoV-2 infection.

show abstract

Section: Inner Canthi Identification and Face Segmentation Algorithmsmentioning

confidence: 99%

An Overview of Thermal Infrared Imaging-Based Screenings during Pandemic Emergencies

Perpetuini

Filippini

Cardone

et al. 2021

IJERPH

View full text Add to dashboard Cite

show abstract

“…Semantic segmentation entails in assigning a specific class to each pixel in an image with the overall aim of discovering objects. It is a key task in the field of computer vision, and has a wide range of applications, including autonomous driving [5], medical research [52], facial recognition [40] and person reidentification [57]. In comparison to other computer vision tasks, the equivalent of this pixel-level label is a difficult and time-consuming effort.…”

Section: Introductionmentioning

confidence: 99%

Self Semi Supervised Neural Architecture Search for Semantic Segmentation

Pauletto¹,

Amini²,

Winckler³

2022

Preprint

View full text Add to dashboard Cite

In this paper, we propose a Neural Architecture Search strategy based on self supervision and semi-supervised learning for the task of semantic segmentation. Our approach builds an optimized neural network (NN) model for this task by jointly solving a jigsaw pretext task discovered with self-supervised learning over unlabeled training data, and, exploiting the structure of the unlabeled data with semi-supervised learning. The search of the architecture of the NN model is performed by dynamic routing using a gradient descent algorithm. Experiments on the Cityscapes and PASCAL VOC 2012 datasets demonstrate that the discovered neural network is more efficient than a state-of-the-art hand-crafted NN model with four times less floating operations.

show abstract

“…In consequence, the foremost deed is to assemble such a technique that could precisely detect COVID-19 during the early stage, in the shortest possible time. There have been many machine learning models that do detect COVID-19 automatically but shortfalls in time check, or even in accurate diagnosis of COVID-19 ( Muhammad, Algehyne, Usman, Ahmad, Chakraborty, Mohammed, 2021 , Müller, Ehlen, Valeske, 2021 , Rasheed, Hameed, Djeddi, Jamil, Al-Turjman, 2021 , Sun, Hong, Song, Li, Wang, 2021 ). As the planet scuffles with COVID-19, every ounce of technical creativity and imagination is deployed to combat this pandemic and bring COVID-19 to an end.…”

Section: Introductionmentioning

confidence: 99%

Optimization and fine-tuning of DenseNet model for classification of COVID-19 cases in medical imaging

Chauhan

Palivela²,

Tiwari

2021

International Journal of Information Management Data Insights

View full text Add to dashboard Cite

It’s been more than a year that the entire world is fighting against Covid-19 pandemic. Starting from the Wuhan city in China, COVID-19 has conquered the entire world with its rapid progression. But seeking the importance towards the human situation, it has become essential to build such an automated model to diagnose COVID-19 within less computational time easily. As the disease has spread, there is not enough data to implement an accurate COVID-19 predicting model. But technology is a boon, which makes it possible. Effective techniques based on medical imaging using artificial intelligence have approached to assist humans in needful time. It has become very essential to detect COVID-19 in humans at an early stage to prevent it from becoming more infectious. The neural networks have shown promising results in medical imaging. In this research, a deep learning-based approach is used for image classification to detect COVID-19 using chest X-ray images (CXR). A CNN classifier have been used to classify the normal-healthy images from the COVID-19 images, using transfer learning. The concept of early stopping is used to enhance the accuracy of the proposed DenseNet model. The results of the system have been evaluated using accuracy, precision, recall and F1-score metrics. An automated comparative analysis among multiple optimizers, LR Scheduler and Loss Function is performed to get the highest accuracy suitable for the proposed system. The Adamax optimizer with Cross Entropy loss function and StepLR scheduler have outperformed with 98.45% accuracy for normal-healthy CXR images and 98.32% accuracy for COVID-19 images.

show abstract

Convolutional Neural Networks for Semantic Segmentation as a Tool for Multiclass Face Analysis in Thermal Infrared

Cited by 20 publications

References 9 publications

An Overview of Thermal Infrared Imaging-Based Screenings during Pandemic Emergencies

An Overview of Thermal Infrared Imaging-Based Screenings during Pandemic Emergencies

Self Semi Supervised Neural Architecture Search for Semantic Segmentation

Optimization and fine-tuning of DenseNet model for classification of COVID-19 cases in medical imaging

Contact Info

Product

Resources

About