scholarly journals Face with Mask Detection in Thermal Images Using Deep Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6387
Author(s):  
Natalia Głowacka ◽  
Jacek Rumiński
Keyword(s):  
Public Health ◽  
Face Detection ◽  
Thermal Imaging ◽  
Deep Neural Networks ◽  
Average Precision ◽  
Thermal Images ◽  
Detection Algorithms ◽  
Health Control ◽  
The Face ◽  
The One

As the interest in facial detection grows, especially during a pandemic, solutions are sought that will be effective and bring more benefits. This is the case with the use of thermal imaging, which is resistant to environmental factors and makes it possible, for example, to determine the temperature based on the detected face, which brings new perspectives and opportunities to use such an approach for health control purposes. The goal of this work is to analyze the effectiveness of deep-learning-based face detection algorithms applied to thermal images, especially for faces covered by virus protective face masks. As part of this work, a set of thermal images was prepared containing over 7900 images of faces with and without masks. Selected raw data preprocessing methods were also investigated to analyze their influence on the face detection results. It was shown that the use of transfer learning based on features learned from visible light images results in mAP greater than 82% for half of the investigated models. The best model turned out to be the one based on Yolov3 model (mean average precision—mAP, was at least 99.3%, while the precision was at least 66.1%). Inference time of the models selected for evaluation on a small and cheap platform allows them to be used for many applications, especially in apps that promote public health.

scholarly journals Facial Recognition for Drunk People Using Thermal Imaging

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Agustin Sancen-Plaza ◽  
Luis M. Contreras-Medina ◽  
Alejandro Israel Barranco-Gutiérrez ◽  
Carlos Villaseñor-Mora ◽  
Juan J Martínez-Nolasco ◽  
...  
Keyword(s):  
Face Recognition ◽  
Surface Temperature ◽  
Thermal Imaging ◽  
Visible Spectrum ◽  
Local Binary Patterns ◽  
Recognition System ◽  
Thermal Images ◽  
Average Percentage ◽  
The Face ◽  
Thermal Face

Face recognition using thermal imaging has the main advantage of being less affected by lighting conditions compared to images in the visible spectrum. However, there are factors such as the process of human thermoregulation that cause variations in the surface temperature of the face. These variations cause recognition systems to lose effectiveness. In particular, alcohol intake causes changes in the surface temperature of the face. It is of high relevance to identify not only if a person is drunk but also their identity. In this paper, we present a technique for face recognition based on thermal face images of drunk people. For the experiments, the Pontificia Universidad Católica de Valparaíso-Drunk Thermal Face database (PUCV-DTF) was used. The recognition system was carried out by using local binary patterns (LBPs). The LBP features were obtained from the bioheat model from thermal image representation and a fusion of thermal images and a vascular network extracted from the same image. The feature vector for each image is formed by the concatenation of the LBP histogram of the thermogram with an anisotropic filter and the fused image, respectively. The proposed technique has an average percentage of 99.63% in the Rank-10 cumulative classification; this performance is superior compared to using LBP in thermal images that do not use the bioheat model.

scholarly journals Fruit Detection and Pose Estimation for Grape Cluster–Harvesting Robot Using Binocular Imagery Based on Deep Neural Networks

2021 ◽  
Vol 8 ◽  
Author(s):  
Wei Yin ◽  
Hanjin Wen ◽  
Zhengtong Ning ◽  
Jian Ye ◽  
Zhiqiang Dong ◽  
...  
Keyword(s):  
Point Cloud ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Model Fitting ◽  
Average Precision ◽  
Detection Algorithms ◽  
Point Cloud Segmentation ◽  
Cylinder Model ◽  
Ransac Algorithm ◽  
Harvesting Robot

Reliable and robust fruit-detection algorithms in nonstructural environments are essential for the efficient use of harvesting robots. The pose of fruits is crucial to guide robots to approach target fruits for collision-free picking. To achieve accurate picking, this study investigates an approach to detect fruit and estimate its pose. First, the state-of-the-art mask region convolutional neural network (Mask R-CNN) is deployed to segment binocular images to output the mask image of the target fruit. Next, a grape point cloud extracted from the images was filtered and denoised to obtain an accurate grape point cloud. Finally, the accurate grape point cloud was used with the RANSAC algorithm for grape cylinder model fitting, and the axis of the cylinder model was used to estimate the pose of the grape. A dataset was acquired in a vineyard to evaluate the performance of the proposed approach in a nonstructural environment. The fruit detection results of 210 test images show that the average precision, recall, and intersection over union (IOU) are 89.53, 95.33, and 82.00%, respectively. The detection and point cloud segmentation for each grape took approximately 1.7 s. The demonstrated performance of the developed method indicates that it can be applied to grape-harvesting robots.

scholarly journals A Joint Learning Approach to Face Detection in Wavelet Compressed Domain

2014 ◽  
Vol 2014 ◽  
pp. 1-13
Author(s):  
Szu-Hao Huang ◽  
Shang-Hong Lai
Keyword(s):  
Face Detection ◽  
Learning Algorithm ◽  
Feature Space ◽  
Detection Algorithm ◽  
Feature Representation ◽  
Compressed Domain ◽  
Discrimination Power ◽  
Detection Algorithms ◽  
The Face ◽  
Active Research

Face detection has been an important and active research topic in computer vision and image processing. In recent years, learning-based face detection algorithms have prevailed with successful applications. In this paper, we propose a new face detection algorithm that works directly in wavelet compressed domain. In order to simplify the processes of image decompression and feature extraction, we modify the AdaBoost learning algorithm to select a set of complimentary joint-coefficient classifiers and integrate them to achieve optimal face detection. Since the face detection on the wavelet compression domain is restricted by the limited discrimination power of the designated feature space, the proposed learning mechanism is developed to achieve the best discrimination from the restricted feature space. The major contributions in the proposed AdaBoost face detection learning algorithm contain the feature space warping, joint feature representation, ID3-like plane quantization, and weak probabilistic classifier, which dramatically increase the discrimination power of the face classifier. Experimental results on the CBCL benchmark and the MIT + CMU real image dataset show that the proposed algorithm can detect faces in the wavelet compressed domain accurately and efficiently.

scholarly journals Evaluation of Face Detection Algorithms for the Bank Client Identity Verification

2017 ◽  
Vol 42 (2) ◽  
pp. 137-148 ◽  
Author(s):  
Maciej Szczodrak ◽  
Andrzej Czyżewski
Keyword(s):  
Face Detection ◽  
Detection Method ◽  
User Interaction ◽  
Identity Verification ◽  
Detection Algorithms ◽  
Video Recordings ◽  
Verification System ◽  
The Face ◽  
Usb Camera ◽  
Made In

Abstract Results of investigation of face detection algorithms efficiency in the banking client visual verification system are presented. The video recordings were made in real conditions met in three bank operating outlets employing a miniature industrial USB camera. The aim of the experiments was to check the practical usability of the face detection method in the biometric bank client verification system. The main assumption was to provide a simplified as much as possible user interaction with the application. Applied algorithms for face detection are described and achieved results of face detection in the real bank environment conditions are presented. Practical limitations of the application based on encountered problems are discussed.

scholarly journals A New Hybrid Approach on Face Detection and Recognition

2020 ◽  
Author(s):  
Saloni Dwivedi ◽  
Nitika Gupta
Keyword(s):  
Face Detection ◽  
Research Work ◽  
Hybrid Approach ◽  
Initial Step ◽  
Detection Algorithms ◽  
Lighting Conditions ◽  
The Face ◽  
Face Detection And Recognition ◽  
Passport Control ◽  
Detection And Recognition

Face detection and recognition is an important paradigm when we consider the biometric based systems. Amongvarious biometric elements, the face is the most reliable one and can be easily observed even from a distance as compared to iris or fingerprint which needs to be closely observed to use them for any kind of detection and recognition. Challenges faced by face detection algorithms often involve the presence of facial features such as beards, moustaches, and glasses, facial expressions,and occlusion of faces like surprised or crying. Another problem is illumination and poor lighting conditions such as in video surveillance cameras image quality and size of the image as in passport control or visa control. Complex backgrounds also make it extremely hard to detect faces. In this research work, a number of methods and research paradigms pertaining to face detection and recognition is studied at length and evaluate various face detection and recognition methods, provide a complete solutionfor image-based face detection and recognition with higher accuracy, a better response rate as an initial step for videosurveillance.

Pulsera inteligente: Watch Ur Health

2021 ◽  
Author(s):  
◽  
A. G. Bravo Sánchez
Keyword(s):  
Public Health ◽  
Potential Risk ◽  
Daily Life ◽  
Technological Development ◽  
Public Health Problem ◽  
Prevention Measures ◽  
Detection Algorithms ◽  
Future Developments ◽  
The Face ◽  
Health Project

The COVID-19 disease continues to be a public health problem that society has integrated into its daily life. Prevention measures are a cornerstone to stop the growing increase in infections. Leading to an opportunity for technological development to create devices that contribute to the reinforcement of these measures. The "Smart Bracelet: Watch Ur Health" project is an innovative proposal for a device capable of warning its users from possible approaches of the hand to the face, which we know are a potential risk to get many infectious diseases, not limited only to COVID-19. This paper reports the development of facial approach detection algorithms and the implementation of a first prototype of the Smart Bracelet, integrating the functions of a common watch. Strategies are proposed for the improvement of functions in future developments.

scholarly journals An Efficient Multi-Scale Anchor Box Approach to Detect Partial Faces from a Video Sequence

2022 ◽  
Vol 6 (1) ◽  
pp. 9
Author(s):  
Dweepna Garg ◽  
Priyanka Jain ◽  
Ketan Kotecha ◽  
Parth Goel ◽  
Vijayakumar Varadarajan
Keyword(s):  
Deep Learning ◽  
Face Detection ◽  
Machine Learning Techniques ◽  
Face Shape ◽  
Detection Algorithms ◽  
Accuracy And Precision ◽  
Learning Techniques ◽  
Proposed Model ◽  
The Face ◽  
Scale Anchor

In recent years, face detection has achieved considerable attention in the field of computer vision using traditional machine learning techniques and deep learning techniques. Deep learning is used to build the most recent and powerful face detection algorithms. However, partial face detection still remains to achieve remarkable performance. Partial faces are occluded due to hair, hat, glasses, hands, mobile phones, and side-angle-captured images. Fewer facial features can be identified from such images. In this paper, we present a deep convolutional neural network face detection method using the anchor boxes section strategy. We limited the number of anchor boxes and scales and chose only relevant to the face shape. The proposed model was trained and tested on a popular and challenging face detection benchmark dataset, i.e., Face Detection Dataset and Benchmark (FDDB), and can also detect partially covered faces with better accuracy and precision. Extensive experiments were performed, with evaluation metrics including accuracy, precision, recall, F1 score, inference time, and FPS. The results show that the proposed model is able to detect the face in the image, including occluded features, more precisely than other state-of-the-art approaches, achieving 94.8% accuracy and 98.7% precision on the FDDB dataset at 21 frames per second (FPS).

scholarly journals Deep Learning Based Object Recognition in Real Time Images Using Thermal Imaging System

2021 ◽  
Author(s):  
Rohini Goel ◽  
Avinash Sharma ◽  
Rajiv Kapoor
Keyword(s):  
Object Recognition ◽  
Real Time ◽  
Thermal Imaging ◽  
Imaging System ◽  
Night Vision ◽  
Driver Assistance ◽  
Thermal Images ◽  
Deep Network ◽  
The One ◽  
Thermal Imaging System

An efficient driver assistance system is essential to avoid mishaps. The collision between the vehicles and objects before vehicle is the one of the principle reason of mishaps that outcomes in terms of diminished safety and higher monetary loss. Researchers are interminably attempting to upgrade the safety means for diminishing the mishap rates. This paper proposes an accurate and proficient technique for identifying objects in front of vehicles utilizing thermal imaging framework. For this purpose, image dataset is obtained with the help of a night vision IR camera. This strategy presents deep network based procedure for recognition of objects in thermal images. The deep network gives the model understanding of real world objects and empowers the object recognition. The real time thermal image database is utilized for the training and validation of deep network. In this work, Faster R-CNN is used to adequately identify objects in real time thermal images. This work can be an incredible help for driver assistance framework. The outcomes exhibits that the proposed work assists to boost public safety with good accuracy.

scholarly journals Facial Recognition Approach using ABCD Algorithm for Cancer Treatment

2019 ◽  
Vol 8 (9) ◽  
pp. 3408-3411
Keyword(s):  
Face Detection ◽  
Specific Area ◽  
Skin Lesions ◽  
Input Image ◽  
Test Case ◽  
Data Sets ◽  
Abnormal Growth ◽  
Detection Algorithms ◽  
Drastic Increase ◽  
The Face

Malignant melanoma is one of the generally known cancers due to the changes in the skin behaviour that cause a drastic increase in numerous melanomas which is seen among many white-skinned people. To detect and classify skin lesions, we require a fast and reliable system. The face detection algorithms are used in which, an image dataset is formed and from that several images are tested for the presence of a face. When the face is present, the image is selected for further processing and separate features are detected. The presence of the face, along with two eyes, nose, mouth and lips are necessary for the face detection to work efficiently. A specific area of the face is selected as a test case and the skin irregularity is checked for abnormal features are present or not. An algorithm by the name Asymmetry, Border, Color and Dermatoscopic features (ABCD) is developed which will check the skin parameters and help figure out the presence of abnormal growth. The accuracy of detection will depend upon the clarity of the input image, the brightness and the sharpness. The later part of the project will stress the importance of data exports from the working data sets to a portable format

Óscar Romero and the Politics of Sainthood

2011 ◽  
Vol 5 (3) ◽  
pp. 265-291
Author(s):  
Manuel A. Vasquez ◽  
Anna L. Peterson
Keyword(s):  
Human Rights ◽  
Civil War ◽  
El Salvador ◽  
The Other ◽  
Post Colonial ◽  
Holy See ◽  
The Poor ◽  
The Face ◽  
Crisis Of Modernity ◽  
The One

In this article, we explore the debates surrounding the proposed canonization of Archbishop Oscar Romero, an outspoken defender of human rights and the poor during the civil war in El Salvador, who was assassinated in March 1980 by paramilitary death squads while saying Mass. More specifically, we examine the tension between, on the one hand, local and popular understandings of Romero’s life and legacy and, on the other hand, transnational and institutional interpretations. We argue that the reluctance of the Vatican to advance Romero’s canonization process has to do with the need to domesticate and “privatize” his image. This depoliticization of Romero’s work and teachings is a part of a larger agenda of neo-Romanization, an attempt by the Holy See to redeploy a post-colonial and transnational Catholic regime in the face of the crisis of modernity and the advent of postmodern relativism. This redeployment is based on the control of local religious expressions, particularly those that advocate for a more participatory church, which have proliferated with contemporary globalization

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