Infrared face recognition: a comprehensive review of methodologies and databases

Automatic face recognition is an area with immense practical potential which includes a wide range of commercial and law enforcement applications. Hence it is unsurprising that 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 face recognition continues to improve, benefitting from advances in a range of different research fields such as image processing, pattern recognition, computer graphics, and physiology. Systems based on visible spectrum images, the most researched face recognition modality, have reached a significant level of maturity with some practical success. However, they continue to face challenges in the presence of illumination, pose and expression changes, as well as facial disguises, all of which can significantly decrease recognition 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. Our key contributions are: (i) a summary of the inherent properties of infrared imaging which makes this modality promising in the context of face recognition, (ii) a systematic review of the most influential approaches, with a focus on emerging common trends as well as key differences between alternative methodologies, (iii) a description of the main databases of infrared facial images available to the researcher, and lastly (iv) a discussion of the most promising avenues for future research.Comment: Pattern Recognition, 2014. arXiv admin note: substantial text overlap with arXiv:1306.160

Face Image Modality Recognition and Photo-Sketch Matching

Face is an important physical characteristic of human body, and is widely used in many crucial applications, such as video surveillance, criminal investigation, and security access system. Based on realistic demand, such as useful face images in dark environment and criminal profile, different modalities of face images appeared, e.g. three-dimensional (3D), near infrared (NIR), and thermal infrared (TIR) face images. Thus, researches with various face image modalities become a hot area. Most of them are set on knowing the modality of face images in advance, which contains a few limitations. In this thesis, we present approaches for face image modality recognition to extend the possibility of cross-modality researches as well as handle new modality-mixed face images. Furthermore, a large facial image database is assembled with five commonly used modalities such as 3D, NIR, TIR, sketch, and visible light spectrum (VIS). Based on the analysis of results, a feature descriptor based on convolutional neural network with linear kernel SVM did an optimal performance.;As we mentioned above, face images are widely used in crucial applications, and one of them is using the sketch of suspect\u27s face, which based on the witness\u27 description, to assist law enforcement. Since it is difficult to capture face photos of the suspect during a criminal activity, automatic retrieving photos based on the suspect\u27s facial sketch is used for locating potential suspects. In this thesis, we perform photo-sketch matching by synthesizing the corresponding pseudo sketch from a given photo. There are three methods applied in this thesis, which are respectively based on style transfer, DualGAN, and cycle-consistent adversarial networks. Among the results of these methods, style transfer based method did a poor performance in photo-sketch matching, since it is an unsupervised one which is not purposeful in photo to sketch synthesis problem while the others need to train pointed models in synthesis stage

Comparison of fusion methods for thermo-visual surveillance tracking

In this paper, we evaluate the appearance tracking performance of multiple fusion schemes that combine information from standard CCTV and thermal infrared spectrum video for the tracking of surveillance objects, such as people, faces, bicycles and vehicles. We show results on numerous real world multimodal surveillance sequences, tracking challenging objects whose appearance changes rapidly. Based on these results we can determine the most promising fusion scheme

Real-time 3D Face Recognition using Line Projection and Mesh Sampling

The main contribution of this paper is to present a novel method for automatic 3D face recognition based on sampling a 3D mesh structure in the presence of noise. A structured light method using line projection is employed where a 3D face is reconstructed from a single 2D shot. The process from image acquisition to recognition is described with focus on its real-time operation. Recognition results are presented and it is demonstrated that it can perform recognition in just over one second per subject in continuous operation mode and thus, suitable for real time operation

Interest Points as a Focus Measure in Multi-Spectral Imaging

A novel multi-spectral focus measure that is based on algorithms for interest point detection, particularly on the FAST (Features from Accelerated Segment Test), Fast Hessian and Harris-Laplace detector, is described in this paper. The proposed measure methods are compared with commonly used focus measure techniques like energy of image gradient, sum-modified Laplacian, Tenenbaum's algorithm or spatial frequency when testing their reliability and performance. The measures have been tested on a newly created database containing 420 images acquired in visible, near-infrared and thermal spectrum (7 objects in each spectrum). Algorithms based on the interest point detectors proved to be good focus measures satisfying all the requirements described in the paper, especially in thermal spectrum. It is shown that these algorithms outperformed all commonly used methods in thermal spectrum and therefore can serve as a new and more accurate focus measure

Polar Fusion Technique Analysis for Evaluating the Performances of Image Fusion of Thermal and Visual Images for Human Face Recognition

This paper presents a comparative study of two different methods, which are based on fusion and polar transformation of visual and thermal images. Here, investigation is done to handle the challenges of face recognition, which include pose variations, changes in facial expression, partial occlusions, variations in illumination, rotation through different angles, change in scale etc. To overcome these obstacles we have implemented and thoroughly examined two different fusion techniques through rigorous experimentation. In the first method log-polar transformation is applied to the fused images obtained after fusion of visual and thermal images whereas in second method fusion is applied on log-polar transformed individual visual and thermal images. After this step, which is thus obtained in one form or another, Principal Component Analysis (PCA) is applied to reduce dimension of the fused images. Log-polar transformed images are capable of handling complicacies introduced by scaling and rotation. The main objective of employing fusion is to produce a fused image that provides more detailed and reliable information, which is capable to overcome the drawbacks present in the individual visual and thermal face images. Finally, those reduced fused images are classified using a multilayer perceptron neural network. The database used for the experiments conducted here is Object Tracking and Classification Beyond Visible Spectrum (OTCBVS) database benchmark thermal and visual face images. The second method has shown better performance, which is 95.71% (maximum) and on an average 93.81% as correct recognition rate.Comment: Proceedings of IEEE Workshop on Computational Intelligence in Biometrics and Identity Management (IEEE CIBIM 2011), Paris, France, April 11 - 15, 201

A mask-based approach for the geometric calibration of thermal-infrared cameras

Accurate and efficient thermal-infrared (IR) camera calibration is important for advancing computer vision research within the thermal modality. This paper presents an approach for geometrically calibrating individual and multiple cameras in both the thermal and visible modalities. The proposed technique can be used to correct for lens distortion and to simultaneously reference both visible and thermal-IR cameras to a single coordinate frame. The most popular existing approach for the geometric calibration of thermal cameras uses a printed chessboard heated by a flood lamp and is comparatively inaccurate and difficult to execute. Additionally, software toolkits provided for calibration either are unsuitable for this task or require substantial manual intervention. A new geometric mask with high thermal contrast and not requiring a flood lamp is presented as an alternative calibration pattern. Calibration points on the pattern are then accurately located using a clustering-based algorithm which utilizes the maximally stable extremal region detector. This algorithm is integrated into an automatic end-to-end system for calibrating single or multiple cameras. The evaluation shows that using the proposed mask achieves a mean reprojection error up to 78% lower than that using a heated chessboard. The effectiveness of the approach is further demonstrated by using it to calibrate two multiple-camera multiple-modality setups. Source code and binaries for the developed software are provided on the project Web site