Thermal Face Recognition Using Moments Invariants

Computer Vision and Image Understanding, 2013

This paper proposes an accurate, rotation invariant, and fast approach for detection of facial features from thermal images. The proposed approach combines both appearance and geometric information to detect the facial features. A texture based detector is performed using Haar features and AdaBoost algorithm. Then the relation between these facial features is modeled using a complex Gaussian distribution, which is invariant to rotation. Experiments show that our proposed approach outperforms existing algorithms for facial features detection in thermal images. The proposed approach's performance is illustrated in a face recognition framework, which is based on extracting a local signature around facial features. Also, the paper presents a comparative study for different signature techniques with different facial image resolutions. The results of this comparative study suggest the minimum facial image resolution in thermal images, which can be used in face recognition. The study also gives a guideline for choosing a good signature, which leads to the best recognition rate.

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.

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.

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000

The current dominant approaches to face recognition rely on facial characteristics that are on or over the skin. Some of these characteristics have low permanency can be altered, and their phenomenology varies significantly with environmental factors (e.g., lighting). Many methodologies have been developed to address these problems to various degrees. However, the current framework of face recognition research has a potential weakness due to its very nature. We present a novel framework for face recognition based on physiological information. The motivation behind this effort is to capitalize on the permanency of innate characteristics that are under the skin. To establish feasibility, we propose a specific methodology to capture facial physiological patterns using the bioheat information contained in thermal imagery. First, the algorithm delineates the human face from the background using the Bayesian framework. Then, it localizes the superficial blood vessel network using image morphology. The extracted vascular network produces contour shapes that are characteristic to each individual. The branching points of the skeletonized vascular network are referred to as Thermal Minutia Points (TMPs) and constitute the feature database. To render the method robust to facial pose variations, we collect for each subject to be stored in the database five different pose images (center, midleft profile, left profile, midright profile, and right profile). During the classification stage, the algorithm first estimates the pose of the test image. Then, it matches the local and global TMP structures extracted from the test image with those of the corresponding pose images in the database. We have conducted experiments on a multipose database of thermal facial images collected in our laboratory, as well as on the time-gap database of the University of Notre Dame. The good experimental results show that the proposed methodology has merit, especially with respect to the problem of low permanence over time. More importantly, the results demonstrate the feasibility of the physiological framework in face recognition and open the way for further methodological and experimental research in the area.

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).

A novel approach to face recognition system based on thermal image is presented. The thermal images are captured using Thermal Mid Wave Infrared (MWIR) camera system at various temperature to take into consideration of subtle variations that may occur over time. The vasculature information from the image is extracted and used for matching process. Four images are used for registration process. The image registration is done with the Rigid Body Image Registration tool. Before registering Gabor filter is applied to the images and then filtered images are fused and registered. Face segmentation is done by localized region based active counters. The anisotropic diffusion filter is applied to an image and fused with the segmented image. The signature is then generated from the segmented image. Then templates are generated and stored for matching purpose. To perform authenticate the signatures are generated first and matched with the templates stored in the system

2016

In last few years, Identification Systems has received a lot of attention in various areas like Academics, Entertainment, Biomedical, Business communities etc. Biometric Identification systems have emerged as a preferred alternative to traditional forms of Identification. Several Biometric modalities research includes Fingerprint, Iris, Face and Retina recognition has got varying level of success. Our system is concerned with Thermal Imaging for Facial recognition. The convection type of heat transfer effect from the flow of hot arterial blood in vessels creates characteristic Thermal imprints [1]. Therefore when one acquires the Thermal image of Subject's face, it actually captures the vein structure of the face because the temperature of the skin is nothing but the temperature of underlying blood vessels [2]. We contribute, through this paper, to the design of thermal imaging framework able to do face recognition with unique feature extraction and Similarity measurements. The ...

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.

Cureus

The technology for deep learning in the field of thermal infrared face recognition has recently become more available for use in research, therefore allowing for the many groups working on this subject to achieve many novel findings. Thermal infrared face recognition helps recognize faces that are not able to be recognized in visible light and can additionally recognize facial blood vessel structure. Previous research regarding temperature variations, mathematical formulas, wave types, and methods in thermal infrared face recognition is reviewed.

International Journal of Pattern Recognition and Artificial Intelligence, 2014

This paper presents a robust approach for recognition of thermal face images based on decision level fusion of 34 different region classifiers. The region classifiers concentrate on local variations. They use singular value decomposition (SVD) for feature extraction. Fusion of decisions of the region classifier is done by using majority voting technique. The algorithm is tolerant against false exclusion of thermal information produced by the presence of inconsistent distribution of temperature statistics which generally make the identification process difficult. The algorithm is extensively evaluated on UGC-JU thermal face database, and Terravic facial infrared database and the recognition performance are found to be 95.83% and 100%, respectively. A comparative study has also been made with the existing works in the literature.