Infrared face recognition: A comprehensive review of methodologies and databases (original) (raw)

Infrared Face Recognition: A Literature Review

International Joint Conference on Neural Networks, 2013

Automatic face recognition (AFR) is an area with immense practical potential which includes a wide range of commercial and law enforcement applications, and it continues to be one of the most active research areas of computer vision. Even after over three decades of intense research, the state-of-the-art in AFR continues to improve, benefiting from advances in a range of different fields including image processing, pattern recognition, computer graphics and physiology. However, systems based on visible spectrum images continue to face challenges in the presence of illumination, pose and expression changes, as well as facial disguises, all of which can significantly decrease their accuracy. Amongst various approaches which have been proposed in an attempt to overcome these limitations, the use of infrared (IR) imaging has emerged as a particularly promising research direction. This paper presents a comprehensive and timely review of the literature on this subject.

Recent advances in visual and infrared face recognition - a review

Computer Vision and Image Understanding, 2005

Face recognition is a rapidly growing research area due to increasing demands for security in commercial and law enforcement applications. This paper provides an up-to-date review of research efforts in face recognition techniques based on two-dimensional (2D) images in the visual and infrared (IR) spectra. Face recognition systems based on visual images have reached a significant level of maturity with some practical success. However, the performance of visual face recognition may degrade under poor illumination conditions or for subjects of various skin colors. IR imagery represents a viable alternative to visible imaging in the search for a robust and practical identification system. While visual face recognition systems perform relatively reliably under controlled illumination conditions, thermal IR face recognition systems are advantageous when there is no control over illumination or for detecting disguised faces. Face recognition using 3D images is another active area of face recognition, which provides robust face recognition with changes in pose. Recent research has also demonstrated that the fusion of different imaging modalities and spectral components can improve the overall performance of face recognition.

State of the art in infrared face recognition

2008

Face recognition is an area that has attracted a lot of interest. Much of the research in this field was conducted using visible images. With visible cameras the recognition is prone to errors due to illumination changes. To avoid the problems encountered in the visible spectrum many authors have proposed the use of infrared. In this paper we give an overview of the state of the art in face recognition using infrared images. Emphasis is given to more recent works. A growing field in this area is multimodal fusion; work conducted in this field is also presented in this paper and publicly available Infrared face image databases are introduced.

Infrared Face Recognition: A Review of the State of the Art

Proceedings of the 2010 International Conference on Quantitative InfraRed Thermography, 2010

In this paper a review of the state of the art has been presented on Infrared face recognition. A comprehensive review of the state of the art has been already done in [1] in 2008. Our review is a complement of the mentioned work [1] with more emphasis given to more recent or more important publications. Initially, we will review the basic important works on Infrared Face Recognition before 2008. Afterwards, we will focus on the recent works which are not reviewed in [1]. Finally, we will draw the conclusions.

Infrared Face Recognition in Forensics via Texture Analysis

International Journal of Computing, Communication and Instrumentation Engineering

Bad lighting was one of the difficulties that facial-recognition systems had to overcome as the large intra-class variations due to pose, lighting, and expressions. Recently, there are many works using thermal cameras in face recognition systems. This paper recommends a thermal face recognition method based on texture analyses in a complex database including different poses and expressions. Three different texture analyses methods are compared in this problem. Due to the pose variations in the database Scale Invariant Feature Transform algorithm is more successful to other methods explained in the paper.

Face Recognition Using Infrared Vision

Thesis

Over the course of the last decade, infrared (IR) and particularly thermal IR imaging based face recognition has emerged as a promising complement to conventional, visible spectrum based approaches which continue to struggle when applied in the real world. While inherently insensitive to visible spectrum illumination changes, IR images introduce specific challenges of their own, most notably sensitivity to factors which affect facial heat emission patterns, e.g., emotional state, ambient temperature, etc. In addition, facial expression and pose changes are more difficult to correct in IR images because they are less rich in high frequency details which is an important cue for fitting any deformable model. In this thesis we describe a novel method which addresses these major challenges. Specifically, to normalize for pose and facial expression changes we generate a synthetic frontal image of a face in a canonical, neutral facial expression from an image of the face in an arbitrary pose and facial expression. This is achieved by piecewise affine warping which follows active appearance model (AAM) fitting. This is the first work which explores the use of an AAM on thermal IR images; we propose a pre-processing step which enhances details in thermal images, making AAM convergence faster and more accurate. To overcome the problem of thermal IR image sensitivity to the exact pattern of facial temperature emissions we describe a representation based on reliable anatomical features. In contrast to previous approaches, our representation is not binary; rather, our method accounts for the reliability of the extracted features. This makes the proposed representation much more robust both to pose and scale changes. The effectiveness of the proposed approach is demonstrated on the largest public database of thermal IR images of faces on which it achieves satisfying recognition performance and significantly outperforms previously described methods. The proposed approach has also demonstrated satisfying performance on subsets of the largest video database of the world gathered in our laboratory which will be publicly available free of charge in future. The reader should note that due to the very nature of the feature extraction method in our system (i.e., anatomical based nature of it), we anticipate high robustness of our system to some challenging factors such as the temperature changes. However, we were not able to investigate this in depth due to the limits which exist in gathering realistic databases. Gathering the largest video database considering some challenging factors is one of the other contributions of this research.

Review of Application of Thermal Imaging for Face Recognition

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.

Thermal Infrared Face Recognition

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.