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

Imaging Fourier transform spectrometers for environmental sensing

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/77073/1/AIAA-1998-291-523.pd

Change detection analysis with spectral thermal imagery

Spectral imagery offers additional information about a scene that can enhance an analyst's ability to conduct change detection. Automation of change detection is required to sift through countless images to identify scenes that have significant intelligence value. Change detection in spectral thermal imagery enables exploitation at night by taking advantage of the emissive characteristics of materials. Data collected from the Spatially Enhanced Broadband Array Spectrograph System (SEBASS) were used to investigate the feasibility of spectral thermal change detection in the long wave infrared (LWIR) region. This study used analysis techniques of differencing, histograms, and principal components analysis to detect spectral changes and investigate the utility of spectral change detection. Many artifacts can influence the sensitivity of change detection methods. Temperature dependence and gross registration errors greatly affect an analysts ability to make use of spectral thermal data for change detection; however, with effort, spectral changes were still detected with these data and suggest that the techniques would be useful once the undesirable characteristics are minimizedhttp://www.archive.org/details/changedetectiona00behrLieutenant, United States NavyApproved for public release; distribution is unlimited

Hyperspectral Imaging for Landmine Detection

This PhD thesis aims at investigating the possibility to detect landmines using hyperspectral imaging. Using this technology, we are able to acquire at each pixel of the image spectral data in hundreds of wavelengths. So, at each pixel we obtain a reflectance spectrum that is used as fingerprint to identify the materials in each pixel, and mainly in our project help us to detect the presence of landmines. The proposed process works as follows: a preconfigured drone (hexarotor or octorotor) will carry the hyperspectral camera. This programmed drone is responsible of flying over the contaminated area in order to take images from a safe distance. Various image processing techniques will be used to treat the image in order to isolate the landmine from the surrounding. Once the presence of a mine or explosives is suspected, an alarm signal is sent to the base station giving information about the type of the mine, its location and the clear path that could be taken by the mine removal team in order to disarm the mine. This technology has advantages over the actually used techniques: • It is safer because it limits the need of humans in the searching process and gives the opportunity to the demining team to detect the mines while they are in a safe region. • It is faster. A larger area could be cleared in a single day by comparison with demining techniques • This technique can be used to detect at the same time objects other than mines such oil or minerals. First, a presentation of the problem of landmines that is expanding worldwide referring to some statistics from the UN organizations is provided. In addition, a brief presentation of different types of landmines is shown. Unfortunately, new landmines are well camouflaged and are mainly made of plastic in order to make their detection using metal detectors harder. A summary of all landmine detection techniques is shown to give an idea about the advantages and disadvantages of each technique. In this work, we give an overview of different projects that worked on the detection of landmines using hyperspectral imaging. We will show the main results achieved in this field and future work to be done in order to make this technology effective. Moreover, we worked on different target detection algorithms in order to achieve high probability of detection with low false alarm rate. We tested different statistical and linear unmixing based methods. In addition, we introduced the use of radial basis function neural networks in order to detect landmines at subpixel level. A comparative study between different detection methods will be shown in the thesis. A study of the effect of dimensionality reduction using principal component analysis prior to classification is also provided. The study shows the dependency between the two steps (feature extraction and target detection). The selection of target detection algorithm will define if feature extraction in previous phase is necessary. A field experiment has been done in order to study how the spectral signature of landmine will change depending on the environment in which the mine is planted. For this, we acquired the spectral signature of 6 types of landmines in different conditions: in Lab where specific source of light is used; in field where mines are covered by grass; and when mines are buried in soil. The results of this experiment are very interesting. The signature of two types of landmines are used in the simulations. They are a database necessary for supervised detection of landmines. Also we extracted some spectral characteristics of landmines that would help us to distinguish mines from background

Target detection, tracking, and localization using multi-spectral image fusion and RF Doppler differentials

It is critical for defense and security applications to have a high probability of detection and low false alarm rate while operating over a wide variety of conditions. Sensor fusion, which is the the process of combining data from two or more sensors, has been utilized to improve the performance of a system by exploiting the strengths of each sensor. This dissertation presents algorithms to fuse multi-sensor data that improves system performance by increasing detection rates, lowering false alarms, and improving track performance. Furthermore, this dissertation presents a framework for comparing algorithm error for image registration which is a critical pre-processing step for multi-spectral image fusion. First, I present an algorithm to improve detection and tracking performance for moving targets in a cluttered urban environment by fusing foreground maps from multi-spectral imagery. Most research in image fusion consider visible and long-wave infrared bands; I examine these bands along with near infrared and mid-wave infrared. To localize and track a particular target of interest, I present an algorithm to fuse output from the multi-spectral image tracker with a constellation of RF sensors measuring a specific cellular emanation. The fusion algorithm matches the Doppler differential from the RF sensors with the theoretical Doppler Differential of the video tracker output by selecting the sensor pair that minimizes the absolute difference or root-mean-square difference. Finally, a framework to quantify shift-estimation error for both area- and feature-based algorithms is presented. By exploiting synthetically generated visible and long-wave infrared imagery, error metrics are computed and compared for a number of area- and feature-based shift estimation algorithms. A number of key results are presented in this dissertation. The multi-spectral image tracker improves the location accuracy of the algorithm while improving the detection rate and lowering false alarms for most spectral bands. All 12 moving targets were tracked through the video sequence with only one lost track that was later recovered. Targets from the multi-spectral tracking algorithm were correctly associated with their corresponding cellular emanation for all targets at lower measurement uncertainty using the root-mean-square difference while also having a high confidence ratio for selecting the true target from background targets. For the area-based algorithms and the synthetic air-field image pair, the DFT and ECC algorithms produces sub-pixel shift-estimation error in regions such as shadows and high contrast painted line regions. The edge orientation feature descriptors increase the number of sub-field estimates while improving the shift-estimation error compared to the Lowe descriptor

Theoretical and practical analysis of spatial and spectral calibration of static Fourier transform infrared spectrometers

International audienceStatic Fourier transform spectrometers require, especially in the infrared, a spatial calibration step. Unfortunately, the superposition of fringes on the measured images has a major impact on spatial calibration and therefore on the returned spectra. We first study how to pre-process images so that spectral errors are minimized. Then, we develop a spectrum formation model that is used to correct those spectral errors. The performance, evaluated on synthetic data, is remarkable and theoretically justifies the use of this calibration concept

Multispectral Metamaterial Detectors for Smart Imaging

The ability to produce a high quality infrared image has significantly improved since its initial development in the 1950s. The first generation consisted of a single pixel that required a two-dimensional raster scan to produce an image. The second generation comprised of a linear array of pixels that required a mechanical sweep to produce an image. The third generation utilizes a two-dimensional array of pixels to eliminate the need for a mechanical sweep. Third generation imaging technology incorporates pixels with single color or broadband sensitivity, which results in \u27black and white\u27 images. The research presented in this dissertation focuses on the development of 4th generation infrared detectors for the realization of a new generation of infrared focal plane array. To achieve this goal, we investigate metamaterials to realize multicolor detectors with enhanced quantum efficiency for similar function to a human retina. The key idea is to engineer the pixel such that it not only has the ability to sense multimodal data such as color, polarization, dynamic range and phase but also the intelligence to transmit a reduced data set to the central processing unit (neurophotonics). In this dissertation, we utilize both a quantum well infrared photodetector (QWIP) and interband cascade detector (ICD) hybridized with a metamaterial absorber for enhanced multicolor sensitivity in the infrared regime. Through this work, along with some design lessons throughout this iterative process, we design, fabricate and demonstrate the first deep-subwavelength multispectral infrared detector using an ultra-thin type-II superlattice (T2-SL) detector coupled with a metamaterial absorber with 7X enhanced quantum efficiency. We also identify useful versus non-useful absorption through a combination of absolute absorption and quantum efficiency measurements. In addition to these research efforts, we also demonstrate a dynamic multicolor metamaterial in the terahertz regime with electronically tunable frequency and gain for the first time. Utilizing an electronically tunable metamaterial, one can design an imaging system that can take multiple spectral responses within one frame for the classification of objects based on their spectral fingerprint.\u2

Realistic texture in simulated thermal infrared imagery

Creating a visually-realistic yet radiometrically-accurate simulation of thermal infrared (TIR) imagery is a challenge that has plagued members of industry and academia alike. The goal of imagery simulation is to provide a practical alternative to the often staggering effort required to collect actual data. Previous attempts at simulating TIR imagery have suffered from a lack of texture—the simulated scenes generally failed to reproduce the natural variability seen in actual TIR images. Realistic synthetic TIR imagery requires modeling sources of variability including surface effects such as solar insolation and convective heat exchange as well as sub-surface effects such as density and water content. This research effort utilized the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model, developed at the Rochester Institute of Technology, to investigate how these additional sources of variability could be modeled to correctly and accurately provide simulated TIR imagery. Actual thermal data were collected, analyzed, and exploited to determine the underlying thermodynamic phenomena and ascertain how these phenomena are best modeled. The underlying task was to determine how to apply texture in the thermal region to attain radiometrically-correct, visually-appealing simulated imagery. Three natural desert scenes were used to test the methodologies that were developed for estimating per-pixel thermal parameters which could then be used for TIR image simulation by DIRSIG. Additional metrics were devised and applied to the synthetic images to further quantify the success of this research. The resulting imagery demonstrated that these new methodologies for modeling TIR phenomena and the utilization of an improved DIRSIG tool improved the root mean-squared error (RMSE) of our synthetic TIR imagery by up to 88%

The development of a low-cost, near infrared, high-temperature thermal imaging system and its application to the retrieval of accurate lava lake temperatures at Masaya volcano, Nicaragua

Near infrared thermal cameras can provide useful low-cost imaging systems for high temperature applications, as an alternative to ubiquitous mid-/long-wavelength infrared systems. Here, we present a new Raspberry Pi-based near infrared thermal camera for use at temperatures of ≈ > 500 °C. We discuss in detail the building of the optical system, calibration using a Sakuma-Hattori model and quantification of uncertainties in remote temperature retrievals. We then present results from the deployment of the system on Masaya Volcano, Nicaragua, where the active lava lake was imaged. Temperatures reached a maximum of 1104 ± 14 °C and the lake radiati ve power output was found to range between 30 and 45 MW. To the best of our knowledge, this is the first published ground-based data on the thermal characteristics of this relatively nascent lava lake, which became visible in late 2015

A Comparative Analysis of Hyperspectral Target Detection Algorithms in the Presence of Misregistered Data

Line scanning hyperspectral imaging systems are capable of capturing accurate spatial and spectral information about a scene. These data can be useful for detecting sub-pixel targets. Such systems, however, may be limited by certain key characteristics in their design. Systems employing multiple spectrometers, or that collect data from multiple focal planes may suffer an inherent misregistration between sets of collected spectral bands. In order to utilize the full spectrum for target detection purposes, the sets of bands must be registered to each other as precisely as possible. Perfect registration is not possible, due to both the sensor design, and variation in sensor orientation during data acquisition. The issue can cause degradation in the performance of various target detection algorithms. An analysis of algorithms is necessary to determine which perform well when working with misregistered data. In addition, new algorithms may need to be developed which are more robust in these conditions. The work set forth in this thesis will improve the registration between spectral bands in a line scanning hyperspectral sensor by using a geometric model of the sensor along with aircraft orientation parameters to pair sets of image pixels based on their ground locations. Synthetic scenes were created and band-to-band misregistration was induced between the VIS and NIR spectral channels to test the performance of various hyperspectral target detection algorithms when applied to misregistered hyperspectral data. The results for this case studied show geometric algorithms perform well using only the VIS portion of the EM spectrum, and do not always benefit from the addition of NIR bands, even for small amounts of misregistration. Stochastic algorithms appear to be more robust than geometric algorithms for datasets with band-to-band misregistration. The stochastic algorithms tested often benefit from the addition of NIR bands, even for large amounts of misregistration