Face recognition in low resolution thermal images

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022

In these times of COVID-19, it is essential to go through thermal screening for checking one's body temperature before entering any premises. However, it is a tiring process as it involves measuring body temperature of all people, one at atime. At the same time, those who carry out thermal screening are required to stand for more than 8 hours a day and check each and every person. This takes a lot of time and effort. So to come up with a solution that can do this job effortlessly, we have built a Facial Recognition Thermal Screening System. The device works by recognizing the face of each person and doing thermal screening to detect the body temperature. If a person is found to have a very high temperature, then the systemwill not allow entry and instead will automatically notify that person to take a COVID-19 test. If the body temperature falls between the required normal temperature range and is found to be okay, then entry is allowed after proper sanitization Keywords: Face Recognition, Covid-19, Thermal Scanning, Raspberry-pi, Thermal Sensor .The temporal variations present in thermal face images are mainly due to different environmental conditions, physiological changes of the subjects, and differences of the infrared detectors' responsivity at the time of the capture, which affect the performance of infrared face recognition systems. To perform this paper, we created two thermal face databases that include capture sessions with real and variable conditions. We also propose two criteria to quantify the temporal variations between data sets. The thermal face recognition systems have been developed using the following five methods: local binary pattern (LBP), Weber linear descriptor (WLD), Gabor jet descriptors, scale invariant feature transform, and speeded up robust features. The results indicate that the local matching-based methods (WLD and LBP) are mostly immune to temporal variations, which is noticeable when the face images have been acquired with a time lapse, while the rest of the methods are clearly affected and are not suitable for practical infrared face recognition.

2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), 2018

The popularity of surveillance systems grows as well as a need for better security systems particularly in a bad lighting conditions or at night. The aim of a security system is to collect as many details as possible to enable a better recognition of persons. In this paper, a comparison of representative thermal face recognition methods will be given, emphasizing their strengths and weaknesses. Then, trends in the development of surveillance and security systems will be outlined such as fusion of visible and thermal images and use of convolutional neural networks. Also, existing challenges of thermal facial recognition and its applications in a real world will be pointed out.

Sensors (Basel, Switzerland), 2021

This paper reports on a new approach to face verification in long-wavelength infrared radiation. Two face images were combined into one double image, which was then used as an input for a classification based on neural networks. For testing, we exploited two external and one homemade thermal face databases acquired in various variants. The method is reported to achieve a true acceptance rate of about 83%. We proved that the proposed method outperforms other studied baseline methods by about 20 percentage points. We also analyzed the issue of extending the performance of algorithms. We believe that the proposed double image method can also be applied to other spectral ranges and modalities different than the face.

Face recognition using different imaging modalities, particularly infrared imaging sensors, has become an area of growing interest. The use of thermal IR images can improve the performance of face recognition in uncontrolled illumination conditions. In this paper, we present a new technique for face recognition based on statistical calculations of thermal images. We propose the use of moments invariants which become one of the most important shape descriptors. The proposed feature vector consists of 11 different moments, where three of them are geometric moments and the rest eight are central geometric moments that offer robustness against variability due to changes in localized regions of the faces. The new method has been tested on a new database comprising of images of different expressions, different lightings, and were taken within different time-lapse. The work is reinforced by a discussion of body and face physiology behind thermal face recognition.

Lecture Notes in Computer Science, 2014

The aim of this article is to compare the performance of well-known visible recognition methods but using the thermal spectrum. Specifically, the work considers two local-matching based methods for face recognition commonly used in visible spectrum: Local Binary Pattern (LBP) and Local Derivative Pattern (LDP). The methods are evaluated and compared using the UCHThermalFace database, which includes evaluation methodology that considers real-world conditions. The comparative study results shown that, contrary to what happens in the visible spectrum, the LBP method obtains the best results from the thermal face recognition. On the other hand, LDP results show that it is not an appropriate descriptor for face recognition systems in the thermal spectrum.

Research Developments in Biometrics and Video Processing Techniques

Automatic face recognition has been comprehensively studied for more than four decades, since face recognition of individuals has many applications, particularly in human-machine interaction and security. Although face recognition systems have achieved a significant level of maturity with some realistic achievement, face recognition still remains a challenging problem due to large variation in face images. Face recognition techniques can be generally divided into three categories based on the face image acquisition methodology: methods that work on intensity images, those that deal with video sequences, and those that require other sensory (like 3D sensory or infra-red imagery) data. Researchers are using thermal infrared images for face recognition. Since thermal infrared images have some advantages over 2D images. In this chapter, an overview of some of the well-known techniques of face recognition using thermal infrared faces are discussed, and some of the drawbacks and benefits of each of these methods mentioned therein are discussed. This chapter talks about some of the most recent algorithms developed for this purpose, and tries to give a brief idea of the state of the art of face recognition technology. The authors propose one approach for evaluating the performance of face recognition algorithms using thermal infrared images. They also note the results of several classifiers on a benchmark dataset (Terravic Facial Infrared Database).

In this paper biometrics is used for the authentication purpose. In biometrics specially, Face recognition is used for this paper. We are collecting some faces stored in the databases used for authentication purpose. Suppose one human got mark in faces it will not allow for authentication for that reason .thermal infrared template algorithm is used to handle the critical situation. Collect the faces by using MWIR Camera after stored in the databases and register the faces using linear image registration tool .using image morphology it generate superficial blood vessels. The extracted vascular network wills procedure contour shape which is characteristic to each individual's .finally it matches original image. Gabor filter is used for future extraction.

https://www.ijrrjournal.com/IJRR_Vol.9_Issue.11_Nov2022/IJRR-Abstract17.html, 2022

Recent advances in facial recognition utilizing infrared as a source are described. Recent research has concentrated on face identification using visible light, with the main issue being that the lighting on the face varies in outside circumstances. Recent studies employ infrared light as a source to produce infrared face pictures to overcome this and increase performance. This is known as a thermal face image, and it is extremely valuable in a variety of application systems. Night surveillance systems and military applications are two applications where night vision comes into picture. The choice of infrared, intensity fluctuation, and angle of incidence all play crucial roles in these applications.

2014 IEEE Symposium on Computer Applications and Industrial Electronics (ISCAIE), 2014

Face detection is an important step for successful face recognition system. Variation in lighting conditions result in degradation in performance of face detection. Thermal images are not affected by variation in illumination. In this paper, we have proposed a novel face detection algorithm by using thermal imaging. In our proposed algorithm, Otsu's thresholding method is used for converting the thermal images into binary form. Horizontal projection of the image is calculated to identify the global minimum that helps in identifying height and width of the head region. We have developed a UTAR-YK Thermal database to verify the performance of the proposed algorithm. Experimental results show that our proposed algorithm has an average accuracy of 92.16%, that is measured by finding the overlapping ratio between the automatically-detected face region and manually drawn face region. An average 1.13 bounding box ratio, which is the comparison of the size of the box between the automatically-detected face region and manually drawn face region.

Several studies have shown that the use of thermal images can solve limitations of visible spectrum based face recognition methods operating in unconstrained environments. The recognition of faces in the thermal domain can be tackled using the histograms of Local Binary Pattern (LBP) features method. The aim of this work is to analyze the advantages and limitations of this method by means of a comparative study against other methods. The analyzed methods were selected by considering their performance in former comparative studies, in addition to being real-time-10 fps or more-to require just one image per person, and to being fully online (no requirements of offline enrollment). Thus, in the analysis the following local-matching based methods are considered: Gabor Jet Descriptors (GJD), Weber Linear Discriminant (WLD) and Local Binary Pattern (LBP). The methods are compared using the UCHThermalFace database. The use of this database allows evaluating the methods in real-world conditions that include natural variations in illumination, indoor/outdoor setup, facial expression, pose, accessories, occlusions, and background. In addition, the fusion of some variants of the methods was evaluated. The main conclusions of the comparative study are: (i) All analyzed methods J. Ruiz-del-Solar (B)