Biometric technologies composed of electro-optical/infrared (EO/IR) sensor systems and advanced matching algorithms are being used in various force protection/security and tactical surveillance applications. To date, most of these sensor systems have been widely used in controlled conditions with varying success (e.g., short range, uniform illumination, cooperative subjects). However the limiting conditions of such systems have yet to be fully studied for long range applications and degraded imaging environments. Biometric technologies used for long range applications will invariably suffer from the effects of atmospheric turbulence degradation. Atmospheric turbulence causes blur, distortion and intensity fluctuations that can severely degrade image quality of electro-optic and thermal imaging systems and, for the case of biometrics technology, translate to poor matching algorithm performance. In this paper, we evaluate the effects of atmospheric turbulence and sensor resolution on biometric matching algorithm performance. We use a subset of the Facial Recognition Technology (FERET) database and a commercial algorithm to analyze facial recognition performance on turbulence degraded facial images. The goal of this work is to understand the feasibility of long-range facial recognition in degraded imaging conditions, and the utility of camera parameter trade studies to enable the design of the next generation biometrics sensor systems.
Differential polarimetry has shown the ability to enhance target signatures by reducing background signatures, thus effectively increasing the signal-to-noise ratio on target. This method has mainly been done for resolved, high contrast targets. For ground-to-air search and tracking of small, slow, airborne targets, the target at range can be sub-pixel and hard to detect against the background sky. Given the unpolarized nature of the thermal emission of the background sky, it should be possible to use differential polarimetry to “filter out” the background, and thus enhance the ability of detecting sub-pixel targets. The first step in testing this hypothesis is to devise a set of surrogate sample targets and measure their polarimetric properties in the thermal IR in both the lab and the field. The goal of this paper is to determine whether or not it is feasible to use differential polarimetry to search, detect, and track small airborne objects.
Atmospheric turbulence causes blur, distortion and intensity fluctuations that can severely degrade image quality of electro-optic and thermal imaging systems. Realistic simulated imagery is needed to evaluate the effects of turbulence and provide input for the evaluation of mitigation and image processing algorithms, since turbulence-based data collections can be cost prohibitive and time consuming. In this work we validate an existing turbulence image simulator against a well-characterized dataset, including resolution targets. The robust dataset was collected through a diurnal cycle for a variety of ranges.
Active imaging systems are currently being developed to increase the target acquisition and identification range performance of electro-optical systems. This paper reports on current efforts to extend the Night Vision Integrated Performance Model (NV-IPM) to include laser radar (LADAR) systems for unresolved targets. Combining this new LADAR modeling capability with existing sensor and environment capabilities already present in NV-IPM will enable modeling and trade studies for military relevant systems.
Polarization filters are commonly used as a means of increasing the contrast of a scene thereby increasing sensor range
performance. The change in the signal to noise ratio (SNR) is a function of the polarization of the target and background, the
type and orientation of the polarization filter(s), and the overall transparency of the filter. However, in the mid-wave and longwave
infrared bands (MWIR and LWIR), the noise equivalent temperature difference (NETD), which directly affects the SNR, is
a function of the filter’s re-emission and its reflected temperature radiance. This paper presents a model, by means of a Stokes
vector input, that can be incorporated into the Night Vision Integrated Performance Model (NV-IPM) in order to predict the
change in SNR, NETD, and noise equivalent irradiance (NEI) for infrared polarimeter imaging systems. The model is then used
to conduct a SNR trade study, using a modeled Stokes vector input, for a notional system looking at a reference target. Future
laboratory and field measurements conducted at Night Vision Electronic Sensors Directorate (NVESD) will be used to update,
validate, and mature the model of conventional infrared systems equipped with polarization filters.
Atmospheric turbulence degrades the range performance of military imaging systems, specifically those intended for long range, ground-to-ground target identification. The recent Defense Advanced Research Projects Agency (DARPA) Super Resolution Vision System (SRVS) program developed novel post-processing system components to mitigate turbulence effects on visible and infrared sensor systems. As part of the program, the US Army RDECOM CERDEC NVESD and the US Army Research Laboratory Computational & Information Sciences Directorate (CISD) collaborated on a field collection and atmospheric characterization of a two-handed weapon identification dataset through a diurnal cycle for a variety of ranges and sensor systems. The robust dataset is useful in developing new models and simulations of turbulence, as well for providing as a standard baseline for comparison of sensor systems in the presence of turbulence degradation and mitigation. In this paper, we describe the field collection and atmospheric characterization and present the robust dataset to the defense, sensing, and security community. In addition, we present an expanded model validation of turbulence degradation using the field collected video sequences.
Stand-off base and force protection surveillance measures primarily rely on electro-optic and thermal imaging technology. Atmospheric turbulence causes blur, distortion and intensity fluctuations that can severely degrade the image quality of these systems. This work explores the effects of turbulence image degradation on the performance of automatic facial recognition software and also looks at the potential benefit of turbulence mitigation algorithms. The goal of this work is to understand the feasibility of long-range facial recognition in degraded imaging conditions. In order to create a large enough database to match against, simulated imagery of different ranges and turbulence conditions were created using a horizontal view turbulence simulator and a subset of the Facial Recognition Technology (FERET) database. The simulated turbulence degraded imagery was then processed with facial recognition software and the results are compared against those from the pristine image set. Finally, the performance of the facial recognition software with turbulence mitigated imagery is also presented.
In the past, autonomic nervous system response has often been determined through measuring Electrodermal Activity
(EDA), sometimes referred to as Skin Conductance (SC). Recent work has shown that high resolution thermal cameras
can passively and remotely obtain an analog to EDA by assessing the activation of facial eccrine skin pores. This paper
investigates a method to distinguish facial skin from non-skin portions on the face to generate a skin-only Dynamic
Mask (DM), validates the DM results, and demonstrates DM performance by removing false pore counts. Moreover,
this paper shows results from these techniques using data from 20+ subjects across two different experiments. In the
first experiment, subjects were presented with primary screening questions for which some had jeopardy. In the second
experiment, subjects experienced standard emotion-eliciting stimuli. The results from using this technique will be shown in relation to data and human perception (ground truth). This paper introduces an automatic end-to-end skin detection approach based on texture feature vectors. In doing so, the paper contributes not only a new capability of tracking facial skin in thermal imagery, but also enhances our capability to provide non-contact, remote, passive, and real-time methods for determining autonomic nervous system responses for medical and security applications.
Atmospheric turbulence is an imaging phenomenon that introduces blur, distortion, and intensity fluctuations that
corrupt image quality and can decrease target acquisition performance. The modeling of imaging sensors requires an
accurate description of turbulence effects. We present two novel methodologies for the measurement of the turbulence
MTF in infrared imagery. First, the structural similarity metric is used to compare pristine and degraded imagery.
Second, contrast modulations of radial bar targets are analyzed to extract an equivalent blur. Human perception tests are
compared against model predictions. The results show that complex turbulence effects can be measured and modeled
with simple MTF blurs.
Proc. SPIE. 7662, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXI
KEYWORDS: Long wavelength infrared, Mid-IR, Short wave infrared radiation, Sensors, Video, Modulation transfer functions, CCD image sensors, Target acquisition, Performance modeling, Received signal strength
Operating environments that US Soldiers and Marines are in have changed, along with the types of tasks that they are
required to perform. In addition, the potential imaging sensor options available have increased. These changes make it
necessary to examine how these new tasks are affected by waveband and time of day. US Army Research, Development
and Engineering Command, Communications Electronics Research Development and Engineering Center, Night Vision
and Electronic Sensor Directorate (NVESD), investigated one such task for several wavebands (MWIR, LWIR, Visible,
and SWIR) and during both day and night. This task involved identification of nine different personnel targets: US
Soldier, US Marine, Eastern-European/Asian Soldier, Urban Insurgent, Rural Insurgent, Hostile Militia, Indigenous
Inhabitant, Contract Laborer, and Reporter. These nine distinct targets were made up from three tactically significant
categories: Friendly Force, Combatant and Neutral/Non-Combatant. A ten second video was taken of an actor dressed
as one of these targets. The actors walk a square pattern, enabling all aspects to be seen in each video clip. Target
characteristics were measured and characteristic dimension, target contrast tabulated. A nine-alternative, forced-choice
human perception test was performed at NVESD. This test allowed NVESD to quantify the ability of observers to
discriminate between personnel targets for each waveband and time of day. The task difficulty criterion, V50, was also
calculated allowing for future modeling using the NVESD sensor performance model.