This PDF file contains the front matter associated with SPIE Proceedings Volume 8371, including the Title Page, Copyright information, Table of Contents, Introduction, and Conference Committee listing.

Laser-induced breakdown spectroscopy (LIBS) has made tremendous progress in becoming a viable technology for rapid bacterial pathogen detection and identification. The significant advantages of LIBS include speed (< 1 sec analysis), portability, robustness, lack of consumables, little to no need for sample preparation, lack of genetic amplification, and the ability to identify all bacterial pathogens without bias (including spore-forms and viable but nonculturable specimens). In this manuscript, we present the latest advances achieved in LIBS-based bacterial sensing including the ability to uniquely identify species from more than five bacterial genera with high-sensitivity and specificity. Bacterial identifications are completely unaffected by environment, nutrition media, or state of growth and accurate diagnoses can be made on autoclaved or UV-irradiated specimens. Efficient discrimination of bacteria at the strain level has been demonstrated. A rapid urinary tract infection diagnosis has been simulated with no sample preparation and a one second diagnosis of a pathogen surrogate has been demonstrated using advanced chemometric analysis with a simple "stop-light" user interface. Stand-off bacterial identification at a 20-m distance has been demonstrated on a field-portable instrument. This technology could be implemented in doctors' offices, clinics, or hospital laboratories for point-of-care medical specimen analysis; mounted on military medical robotic platforms for in-the- field diagnostics; or used in stand-off configuration for remote sensing and detection.

We investigated the potential of breath analysis by gas chromatography - mass spectrometry (GC-MS) to discriminate between samples collected prospectively from patients with suspected tuberculosis (TB). Samples were obtained in a TB endemic setting in South Africa where 28% of the culture proven TB patients had a Ziehl-Neelsen (ZN) negative sputum smear. A training set of breath samples from 50 sputum culture proven TB patients and 50 culture negative non-TB patients was analyzed by GC-MS. A classification model with 7 compounds resulted in a training set with a sensitivity of 72%, specificity of 86% and accuracy of 79% compared with culture. The classification model was validated with an independent set of breath samples from 21 TB and 50 non-TB patients. A sensitivity of 62%, specificity of 84% and accuracy of 77% was found. We conclude that the 7 volatile organic compounds (VOCs) that discriminate breath samples from TB and non-TB patients in our study population are probably host-response related VOCs and are not derived from the VOCs secreted by M. tuberculosis. It is concluded that at present GC-MS breath analysis is able to differentiate between TB and non-TB breath samples even among patients with a negative ZN sputum smear but a positive culture for M. tuberculosis. Further research is required to improve the sensitivity and specificity before this method can be used in routine laboratories.

It is a common practice in resource-constrained countries to accept two positive rapid HIV antibody test results as diagnostic for HIV infection. Because these tests are inexpensive and results are obtained quickly, they are recommended by the WHO to "scale-up" HIV testing to increase the number of people tested. The negative predictive value of rapid HIV tests is so high that negative results are considered conclusive despite the fact that false-negative results can occur in several situations. While the specificity and sensitivity of rapid HIV tests in resource-rich countries is acceptable, there are only limited data about their performance in resource-constrained countries. The challenges of rapid HIV testing in these situations will be discussed.

A mathematical model for dengue with three states of infection is proposed and analyzed. The model consists in a system of differential equations. The three states of infection are respectively asymptomatic, partially asymptomatic and fully asymptomatic. The model is analyzed using computer algebra software, specifically Maple, and the corresponding basic reproductive number and the epidemic threshold are computed. The resulting basic reproductive number is an algebraic synthesis of all epidemic parameters and it makes clear the possible control measures. The microscopic structure of the epidemic parameters is established using the quantum theory of the interactions between the atoms and radiation. In such approximation, the human individual is represented by an atom and the mosquitoes are represented by radiation. The force of infection from the mosquitoes to the humans is considered as the transition probability from the fundamental state of atom to excited states. The combination of computer algebra software and quantum theory provides a very complete formula for the basic reproductive number and the possible control measures tending to stop the propagation of the disease. It is claimed that such result may be important in military medicine and the proposed method can be applied to other vector-borne diseases.

The repair of traumatic injuries to the central nervous system remains among the most challenging and exciting frontiers in medicine. In both traumatic brain injury and spinal cord injuries, the ultimate goals are to minimize damage and foster recovery. Numerous DARPA initiatives are in progress to meet these goals. The PREventing Violent Explosive Neurologic Trauma program focuses on the characterization of non-penetrating brain injuries resulting from explosive blast, devising predictive models and test platforms, and creating strategies for mitigation and treatment. To this end, animal models of blast induced brain injury are being established, including swine and non-human primates. Assessment of brain injury in blast injured humans will provide invaluable information on brain injury associated motor and cognitive dysfunctions. The Blast Gauge effort provided a device to measure warfighter's blast exposures which will contribute to diagnosing the level of brain injury. The program Cavitation as a Damage Mechanism for Traumatic Brain Injury from Explosive Blast developed mathematical models that predict stresses, strains, and cavitation induced from blast exposures, and is devising mitigation technologies to eliminate injuries resulting from cavitation. The Revolutionizing Prosthetics program is developing an avant-garde prosthetic arm that responds to direct neural control and provides sensory feedback through electrical stimulation. The Reliable Neural-Interface Technology effort will devise technologies to optimally extract information from the nervous system to control next generation prosthetic devices with high fidelity. The emerging knowledge and technologies arising from these DARPA programs will significantly improve the treatment of brain and spinal cord injured patients.

Attention is a core function in cognition and also the most prevalent cognitive deficit in mild traumatic brain injury (mTBI). Predictive timing is an essential element of attention functioning because sensory processing and execution of goal-oriented behavior are facilitated by temporally accurate prediction. It is hypothesized that impaired synchronization between prediction and external events accounts for the attention deficit in mTBI. Other cognitive and somatic or affective symptoms associated with mTBI may be explained as secondary consequences of impaired predictive timing. Eye-Tracking Rapid Attention Computation (EYE-TRAC) is the quantification of predictive timing with indices of dynamic visuo-motor synchronization (DVS) between the gaze and the target during continuous predictive visual tracking. Such quantification allows for cognitive performance monitoring in comparison to the overall population as well as within individuals over time. We report preliminary results of normative data and data collected from subjects with a history of mTBI within 2 weeks of injury and post-concussive symptoms at the time of recruitment. A substantial proportion of mTBI subjects demonstrated DVS scores worse than 95% of normal subjects. In addition, longitudinal monitoring of acute mTBI subjects showed that initially abnormal DVS scores were followed by improvement toward the normal range. In summary, EYE-TRAC provides fast and objective indices of DVS that allow comparison of attention performance to a normative standard and monitoring of within-individual changes.

As in the general population, chronic pain is a prevalent and burdensome affliction in active-duty military personnel. Painful conditions in military members can be categorized broadly in terms of whether they arise directly from combat injuries (gunshot, fragmentation wound, blast impact) or whether they result from non-combat injuries (sprains, herniated discs, motor vehicle accidents). Both combat-related and non-combat-related causes of pain can further be classified as either acute or chronic. Here we discuss the state of pain management as it relates to the military population in both deployed and non-deployed settings. The term non-battle injury (NBI) is commonly used to refer to those conditions not directly associated with the combat actions of war. In the history of warfare, NBI have far outstripped battle-related injuries in terms not only of morbidity, but also mortality. It was not until improvements in health care and field medicine were applied in World War I that battle-related deaths finally outnumbered those attributed to disease and pestilence. However, NBI have been the leading cause of morbidity and hospital admission in every major conflict since the Korean War. Pain remains a leading cause of presentation to military medical facilities, both in and out of theater. The absence of pain services is associated with a low return-to-duty rate among the deployed population. The most common pain complaints involve the low-back and neck, and studies have suggested that earlier treatment is associated with more significant improvement and a higher return to duty rate. It is recognized that military medicine is often at the forefront of medical innovation, and that many fields of medicine have reaped benefit from the conduct of war.

Doctors must frequently make decisions during medical treatment, whether in an acute care facility, such as an Intensive Care Unit (ICU), or in an operating room. These decisions rely on a various information sources, such as the patient's medical history, preoperative images, and general medical knowledge. Decision support systems can assist by facilitating access to this information when and where it is needed. This paper presents some research eorts that address the integration of information with clinical practice. The example systems include a clinical decision support system (CDSS) for pediatric traumatic brain injury, an augmented reality head- mounted display for neurosurgery, and an augmented reality telerobotic system for minimally-invasive surgery. While these are dierent systems and applications, they share the common theme of providing information to support clinical decisions and actions, whether the actions are performed with the surgeon's own hands or with robotic assistance.

The prevalence of multidrug-resistant infections in personnel wounded in Iraq and Afghanistan has made it challenging for physicians to choose effective therapeutics in a timely fashion. To address the challenge of identifying the potential for drug resistance, we have developed the Antimicrobial Resistance Determinant Microarray (ARDM) to provide DNAbased analysis for over 250 resistance genes covering 12 classes of antibiotics. Over 70 drug-resistant bacteria from different geographic regions have been analyzed on ARDM, with significant differences in patterns of resistance identified: genes for resistance to sulfonamides, trimethoprim, chloramphenicol, rifampin, and macrolide-lincosamidesulfonamide drugs were more frequently identified in isolates from sources in Iraq/Afghanistan. Of particular concern was the presence of genes responsible for resistance to many of the last-resort antibiotics used to treat war traumaassociated infections.

Feasibility of a field-deployable device for the rapid and early diagnosis of cyanide poisoning in whole blood using the spectral shift of the vitamin B12 precursor cobinamide upon binding with cyanide as an indicator is being assessed. Cyanide is an extremely potent and rapid acting poison with as little as 50 mg fatal to humans. Cyanide poisoning has been recognized as a threat from smoke inhalation and potentially through weapons of mass destruction. Currently, no portable rapid tests for the detection of cyanide in whole blood are available. Cobinamide has an extremely high affinity for cyanide and captures hemoglobin associated cyanide from red blood cells. Upon binding of cyanide, cobinamide undergoes a spectral shift that can be measured with a spectrophotometer. We have combined the unique cyanide-binding properties of cobinamide with blood separation technology, sample transport and a detection system, and are developing a rapid, field deployable, disposable device which will deliver an intuitive result to a first responder, allowing for rapid response to exposure events. Feasibility of the cobinamide-Cyanide chemistry in a rapid test using a whole blood sample from a finger-stick has been demonstrated with an assay time from sample collection to a valid result of under 5 minutes. Data showing the efficacy of the diagnostic method and initial device design concepts will be shown.

Novel nanotextured Au surfaces are presented with periodically self-aligned nanopyramid structures with precisely defined pitch that are closely packed with 2 nm separation gaps over large areas and form high-density (~1 km cm^-2) arrays of hot-spot scattering sites ideally suited for surface-enhanced Raman scattering (SERS) and Raman spectroscopy. Average Raman enhancement factors from physically adsorbed rhodamine 6G on patterned Au surfaces resulted in EF~10⁶. The Raman average EF has been characterized over large areas using benzenethiol monolayers chemisorbed on the Au nanopyramid surfaces. From the 1074 cm^-1 ring mode of BT on surfaces with 200 nm pitch the EF=(0.8±0.04)×10⁶, and for surfaces with 500 nm pitch the EF=(0.32±0.01)×10⁷ from over 99% of the imaged area. Maximum EF>10⁸ have been measured in both cases.

In the medical community, patient simulators are used to educate and train nurses, medics and doctors in rendering dierent levels of treatment and care to various patient populations. Students have the opportunity to perform real-world medical procedures without putting any patients at risk. A new thrust for the U.S. Army RDECOM CERDEC Night Vision and Electronic Sensors Directorate (NVESD), is the use of remote sensing technologies to detect human vital signs at stando distances. This capability will provide medics with the ability to diagnose while under re in addition to helping them to prioritize the care and evacuation of battleeld casualties. A potential alternative (or precursor) to human subject testing is the use of patient simulators. This substitution (or augmenting) provides a safe and cost eective means to develop, test, and evaluate sensors without putting any human subjects at risk. In this paper, we present a generalized framework that can be used to accredit patient simulator technologies as human simulants for remote physiological monitoring (RPM). Results indicate that we were successful in using a commercial Laser Doppler Vibrometer (LDV) to exploit pulse and respiration signals from a SimMan 3G patient simulator at stando (8 meters).

Compared to open surgery, minimal invasive surgery offers reduced trauma and faster recovery. However, lack of direct view limits space perception. Stereo-endoscopy improves depth perception, but is still restricted to the direct endoscopic field-of-view. We describe a novel technology that reconstructs 3D-panoramas from endoscopic video streams providing a much wider cumulative overview. The method is compatible with any endoscope. We demonstrate that it is possible to generate photorealistic 3D-environments from mono- and stereoscopic endoscopy. The resulting 3D-reconstructions can be directly applied in simulators and e-learning. Extended to real-time processing, the method looks promising for telesurgery or other remote vision-guided tasks.

Global Assimilation of Information for Action (GAIA) is a new initiative at The Johns Hopkins University connecting decision-makers with the research community. GAIA's focus is on the near- and long-term effects of weather, climate, and climate disruption on society and national security. The GAIA initiative, http://gaia.jhuapl.edu, makes use of collaborative tools to bring together decision makers to address focused problems in settings that range from symposia and workshops to specific socio-political-economic "games" to explore how decisions can be made and risks assessed. GAIA includes a suite of visualization tools, documentation, analyses, and social networking capabilities. Here, we will discuss the GAIA collaboration and recent GAIA projects, in particular the development of climate change national security gaming scenarios and studies in public health, and how the GAIA project can aide in assessing national security and public health concerns.

Public health surveillance is undergoing a revolution driven by advances in the field of information technology. Many countries have experienced vast improvements in the collection, ingestion, analysis, visualization, and dissemination of public health data. Resource-limited countries have lagged behind due to challenges in information technology infrastructure, public health resources, and the costs of proprietary software. The Suite for Automated Global Electronic bioSurveillance (SAGES) is a collection of modular, flexible, freely-available software tools for electronic disease surveillance in resource-limited settings. One or more SAGES tools may be used in concert with existing surveillance applications or the SAGES tools may be used en masse for an end-to-end biosurveillance capability. This flexibility allows for the development of an inexpensive, customized, and sustainable disease surveillance system. The ability to rapidly assess anomalous disease activity may lead to more efficient use of limited resources and better compliance with World Health Organization International Health Regulations.

Exposure assessment is an integral component of occupational and environmental epidemiology, risk assessment and management, as well as regulatory compliance. For the most part, air sampling and analysis tools used in occupational and environmental exposure assessments are based on technologies that have changed little since the 1970s. In many cases the lack of simple, inexpensive, exposure assessment technologies has limited epidemiologists' and risk assessors' ability to evaluate the environmental and occupational causes of disease. While there have been tremendous investments and advances in medical diagnostic and biomonitoring technologies (e.g., glucose testing, human genetics), there has been less effort invested in advancing the science of exposure assessment. Recent developments in sensor technology have focused on medical and homeland security applications. Developing and applying new sensors to health effects studies can revolutionize the way epidemiologic studies are conducted. Time-series studies that investigate short-term (hours to days) changes in exposure that are linked to changes in health care encounters, symptoms, and biological markers of preclinical disease and/or susceptibility are needed to more fully evaluate the impact of chemicals and other agents on health. Current sampling technology limits our ability to assess time-varying concentrations. The purpose of this paper is to discuss the current state of air sampling and health assessment and the potential application of novel sensor technology for use in health effects studies.

Traditionally, human toxicant bioavailability screening has been forced to proceed in either a high throughput fashion using prokaryotic or lower eukaryotic targets with minimal applicability to humans, or in a more expensive, lower throughput manner that uses fluorescent or bioluminescent human cells to directly provide human bioavailability data. While these efforts are often sufficient for basic scientific research, they prevent the rapid and remote identification of potentially toxic chemicals required for modern biosecurity applications. To merge the advantages of high throughput, low cost screening regimens with the direct bioavailability assessment of human cell line use, we re-engineered the bioluminescent bacterial luciferase gene cassette to function autonomously (without exogenous stimulation) within human cells. Optimized cassette expression provides for fully endogenous bioluminescent production, allowing continuous, real time monitoring of the bioavailability and toxicology of various compounds in an automated fashion. To access the functionality of this system, two sets of bioluminescent human cells were developed. The first was programed to suspend bioluminescent production upon toxicological challenge to mimic the non-specific detection of a toxicant. The second induced bioluminescence upon detection of a specific compound to demonstrate autonomous remote target identification. These cells were capable of responding to μM concentrations of the toxicant n-decanal, and allowed for continuous monitoring of cellular health throughout the treatment process. Induced bioluminescence was generated through treatment with doxycycline and was detectable upon dosage at a 100 ng/ml concentration. These results demonstrate that leveraging autonomous bioluminescence allows for low-cost, high throughput direct assessment of toxicant bioavailability.

The Iridium NEXT satellite constellation has designed space to accommodate hosted payloads that provided not only access to space but also allow the user to leverage Iridium's real time communication capability. This is ideal for small sensor payloads and mission areas that require real-time data. The detection of volcanic ash is one such application, meeting a critical need of warning aircraft on the location of volcanic ash. To this end, we have described a system concept that uses small lightweight sensors the fit within the Iridium NEXT hosted payload allocation and provide critical data needed to predict the location and movement of volcanic ash in the atmosphere.

Despite the fact that therapeutic approaches and diagnostic capabilities have made tremendous advances in the past few decades, the associated costs with these treatments continue to rise. This fact, coupled with a rapidly aging population, threatens to cripple our nation's capability to deliver quality healthcare at reasonable and affordable price points. The research community must therefore look to implementing transformational approaches that revolutionize both the way we diagnose and treat patients. Emerging multi-disciplinary research in the fields of molecular biology, systems biology, and solid-state sensing is poised to make such a contribution. Here we highlight key critical insights in the field of human derived volatile organic compound (VOC) signatures and the potential for non-invasive diagnostics. With the aim of developing future VOC-based diagnostics, we identify some critical gaps in our knowledge of how these often complex signatures are influenced by genetics, physiological state, and population variance. Also, we highlight a few canine and solid-state sensing strategies to demonstrate that VOC-based breath diagnostics are feasible and we suggest a bio-inspired approach for optimizing sensor architectures. VOC based diagnostics should drastically enhance early detection of multiple diseases, increase the time for therapeutic intervention, provide the capability to monitor in real-time the efficacy of therapeutic treatments, provide the context of emerging pathological outbreaks across participating populations, and potentially decrease mortality associated with many diseases by orders of magnitude.

Here we describe a prototype for non-destructive, in-situ, accurate and cost-effectively measurement procedure of carbon in soil based on neutron activation analysis using 14 MeV tagged neutron beam. This technology can be used for carbon baseline assessment on regional scale and for monitoring of its surface and depth storage due to the changes in agricultural practices undertaken in order to mitigate global climate change.

This work shows the process of computing coupling coefficients of first-order distributed feedback (DFB) metalsemiconductor quantum-well lasers. For the gas sensing of carbon dioxide (CO₂), the antimonite-based (Sb-based) DFB lasers at the wavelength of 2 μm are discussed. The optical waveguide structure, for each laser, has a built-in grating interface between the metal and semiconductor layers. This work considers the interface with sinusoidal corrugation geometry for preliminary modeling and computation. To compute the coupling coefficient of the metal-grating waveguide, a photonic method, Floquet-Bloch method (FB), is used for the waveguide with such a corrugated semiconductor-metal interface. The optical method, the Ray-optics method (RO), is also used for computation. Both the photonic and the optical methods have close results.

A continuous wave (CW) 94-GHz millimeter wave (mmW) standoff biosensor has been developed for remote biometric sensing applications. The sensor measures the demodulated in-phase (I) and quadrature-phase (Q) components of the received reflected mmW signal from a subject. Both amplitude and phase of the reflected signal are obtained from downconverted I and Q channels from the quadrature mixer. The mmW sensor can faithfully monitor human vital signs (heartbeat and respiration) at relatively long standoff distances. Principle Component Analysis (PCA) is used to extract the heartbeat, the respiration and the body motion signals. The approach allows one to deduce information about amplitude and beat-to-beat rate of the respiration and the heartbeat. Experimental results collected from a subject were analyzed and compared to the signal obtained with a three-electrode ECG monitoring instrument.

We present a unique, lightweight, compact, low-cost, x-ray imager: MiniMAX (Miniature, Mobile, Agile, X-ray). This system, which exploits the best aspects of Computed Radiography (CR) and Digital Radiography (DR) technology, weighs less than 6lbs, fits into a 6" diameter x 16" long carbon-fiber tube, and is constructed almost entirely from offthe- shelf components. MiniMAX is suitable for use in weld inspection, archaeology, homeland security, and veterinary medicine. While quantum limited for MeV radiography, the quantum-efficiency is too low for routine medical use. Formats include: 4"x6", 8"x12", or 16"x24" and can be readily displayed on the camera back, using a pocket projector, or on a tablet computer. In contrast to a conventional, flying-spot scanner, MiniMAX records a photostimulated image from the entire phosphor at once using a bright, red LED flash filtered through an extremely efficient (OD>9) dichroic filter.

Advancements in biological and chemical warfare has allowed for the creation of novel toxins necessitating a universal, real-time sensor. We have used a function-based biosensor employing impedance spectroscopy using a low current density AC signal over a range of frequencies (62.5 Hz-64 kHz) to measure the electrical impedance of a confluent epithelial cell monolayer at 120 sec intervals. Madin Darby canine kidney (MDCK) epithelial cells were grown to confluence on thin film interdigitated gold electrodes. A stable impedance measurement of 2200 Ω was found after 24 hrs of growth. After exposure to cytotoxins anthrax lethal toxin and etoposide, the impedance decreased in a linear fashion resulting in a 50% drop in impedance over 50hrs showing significant difference from the control sample (~20% decrease). Immunofluorescent imaging showed that apoptosis was induced through the addition of toxins. Similarities of the impedance signal shows that the mechanism of cellular death was the same between ALT and etoposide. A revised equivalent circuit model was employed in order to quantify morphological changes in the cell monolayer such as tight junction integrity and cell surface area coverage. This model showed a faster response to cytotoxin (2 hrs) compared to raw measurements (20 hrs). We demonstrate that herein that impedance spectroscopy of epithelial monolayers serves as a real-time non-destructive sensor for unknown pathogens.

Lake Erie is one of the five great lakes of North America. It is the shallowest, the warmest, and the most biologically productive of the Great Lakes producing more fish than all of the other four lakes combined. It is also a source of drinking water for 11 million people and a recreational asset. On the flipside, it is also very vulnerable and troubled with environmental challenges because it has the smallest water volume, but the greatest pressures from the human settlement. One of the many issues faced by the Lake is pollution. It receives larger loads of many pollutants than any other Great Lake. Even with the best pollution controls many pesticides and organohalogens continue to enter the lake. Polybrominated diphenyl ethers (PBDEs) are a class of flame-retardants that have been used in a variety of consumer products since the 1970s. They are added to many commercial and household products such as computers, foam mattresses, carpets, etc. Being largely non-polar and chemically stable, these chemicals are extremely lipophilic and resist degradation in the environment, thus giving them a high affinity for their bioaccumulation. Due to these properties PBDEs have become ubiquitous environmental contaminants. These compounds are reported to be endocrine disruptors and could cause oxidative damage. This report presents the sample preparation protocol, the GC-MS analysis of PBDEs in Lake Erie sediment samples.

Malaria transmission in many part of the world specifically in Bangladesh and southern African countries is unstable and epidemic. An estimate of over a million cases is reported annually. Malaria is heterogeneous, potentially due to variations in ecological settings, socio-economic status, land cover, and agricultural practices. Malaria control only relies on treatment and supply of bed networks. Drug resistance to these diseases is widespread. Vector control is minimal. Malaria control in those countries faces many formidable challenges such as inadequate accessibility to effective treatment, lack of trained manpower, inaccessibility of endemic areas, poverty, lack of education, poor health infrastructure and low health budgets. Health facilities for malaria management are limited, surveillance is inadequate, and vector control is insufficient. Control can only be successful if the right methods are used at the right time in the right place. This paper aims to improve malaria control by developing malaria risk maps and risk models using satellite remote sensing data by identifying, assessing, and mapping determinants of malaria associated with environmental, socio-economic, malaria control, and agricultural factors.

A pathogen has the possibility to attack the human body causing diseases or epidemics depending of our defenses. Government and control health organizations have an especial interest in to know what the probability of the human is and a population to be attacked and infected by the pathogens and how it can be influenced by the virulence and concentration of the external agent or substance. In this way, physics has been involved in the solution of the problem giving to the global health institutions a guide to control the disease, by understanding the situation as a quantum phenomenon known as penetration of a potential barrier or as classical diffusion problem which involves the spatial and temporal variation of the concentration of the pathogen that causes the disease. The computations required to the solution of this problem are presented with MAPLE and is expected that this solutions have important implications to the global health care.

The problem of face identication in the Mid-Wave InfraRed (MWIR) spectrum is studied in order to understand the performance of intra-spectral (MWIR to MWIR) and cross-spectral (visible to MWIR) matching. The contributions of this work are two-fold. First, a database of 50 subjects is assembled and used to illustrate the challenges associated with the problem. Second, a set of experiments is performed in order to demonstrate the possibility of MWIR intra-spectral and cross-spectral matching. Experiments show that images captured in the MWIR band can be eciently matched to MWIR images using existing techniques (originally not designed to address such a problem). These results are comparable to the baseline results, i.e., when comparing visible to visible face images. Experiments also show that cross-spectral matching (the heterogeneous problem, where gallery and probe sets have face images acquired in dierent spectral bands) is a very challenging problem. In order to perform cross-spectral matching, we use multiple texture descriptors and demonstrate that fusing these descriptors improves recognition performance. Experiments on a small database, suggests that the problem of cross-spectral matching requires further investigation.

In low light conditions, visible light face identification is infeasible due to the lack of illumination. For nighttime surveillance, thermal imaging is commonly used because of the intrinsic emissivity of thermal radiation from the human body. However, matching thermal images of faces acquired at nighttime to the predominantly visible light face imagery in existing government databases and watch lists is a challenging task. The difficulty arises from the significant difference between the face's thermal signature and its visible signature (i.e. the modality gap). To match the thermal face to the visible face acquired by the two different modalities, we applied face recognition algorithms that reduce the modality gap in each step of face identification, from low-level analysis to machine learning techniques. Specifically, partial least squares-discriminant analysis (PLS-DA) based approaches were used to correlate the thermal face signatures to the visible face signatures, yielding a thermal-to-visible face identification rate of 49.9%. While this work makes progress for thermal-to-visible face recognition, more efforts need to be devoted to solving this difficult task. Successful development of a thermal-to-visible face recognition system would significantly enhance the Nation's nighttime surveillance capabilities.

Fingerprint identification is a well-regarded and widely accepted modality in the field of biometrics for its high recognition rates. Legacy 2D contact based methods, though highly evolved in terms of technology suffer from certain drawbacks. Being contact based, there are many known issues which affect the recognition rates. Flashscan3D/University of Kentucky (UKY) developed state of the art 3D non-contact fingerprint scanners using different structured light illumination (SLI) techniques namely SLI single Point Of View (POV) and the SLI Subwindowing techniques. Capturing the fingerprints by non-contact means in 3D gives much higher quality fingerprint data which ultimately improves matching rates over a traditional 2D approach. In this paper, we present a full hand 3D non-contact scanner using the SLI Sub-windowing technique. Sample fingerprint data and experimental results for fingerprint matching based on a small sample 3D fingerprint test set are presented.

Iris recognition is considered to be one of the most accurate biometrics, but user inconvenience during the image acquisition phase has limited its widespread use. Image capture is largely constrained to well-controlled situations, where subjects must remain relatively stationary or within a capture "volume" close to the camera. As a consequence, iris recognition systems have a reputation for being borderline intrusive, and less friendly for both subjects and operators. To support the development of a more natural and acceptable iris capture system, we have sought to develop a pre-processor driven imaging system that predicts a maximal opportunity window for iris capture for a subject engaged in natural motion based on predictive head and eye movement algorithms. This paper describes a first-generation prototype iris capture system that utilizes this approach. A wide field of view camera is used to track a person's face and provide head pose data as input to the predictive algorithm. The algorithm is then used to direct a second narrow field of view camera to capture the iris image more reliably. This system serves as a platform for further development of head movement prediction algorithms used to enhance the probability of iris capture in moving or uncooperative subjects.

In this work, an integrated contactless multiple hand feature acquisition system is designed. The system can capture palmprint, palm vein, and palm dorsal vein images simultaneously. Moreover, the images are captured in a contactless manner, that is, users need not to touch any part of the device when capturing. Palmprint is imaged under visible illumination while palm vein and palm dorsal vein are imaged under near infrared (NIR) illumination. The capturing is controlled by computer and the whole process is less than 1 second, which is sufficient for online biometric systems. Based on this device, this paper also implements a contactless hand-based multimodal biometric system. Palmprint, palm vein, palm dorsal vein, finger vein, and hand geometry features are extracted from the captured images. After similarity measure, the matching scores are fused using weighted sum fusion rule. Experimental results show that although the verification accuracy of each uni-modality is not as high as that of state-of-the-art, the fusion result is superior to most of the existing hand-based biometric systems. This result indicates that the proposed device is competent in the application of contactless multimodal hand-based biometrics.

This paper introduces a person identification system that uses as input the shadow images of a walking person, as projected by multiple lights(in this application invisible/infrared lights); the system uses a database of examples of shadows images of a number of people who walk. While it is accepted that personal identification has a higher correct classification rate if views from multiple cameras are used, most systems use only one camera, mainly because (i) Installation in real-world environments is easier, less cameras and no need to synchronize cameras, (ii) Computational cost is reduced. In the proposed system, we obtain the advantages of multiple viewpoints with a single camera and additional light sources. More specific, we install multiple infrared lights to project shadows of a subject on the ground and a camera with an infrared transmitting filter mounted in the ceiling inside of a building. Shadow areas, which are projections of one's body on the ground by multiple lights, can be considered as body areas captured from different viewpoints; thus, the proposed system is able to capture multiple projections of the body from a single camera. We explored in other papers the use of sunproduced shadow for identification of people walking freely in the outdoor. In this paper the application scenario is a system installed at the airport in the areas that precedes the immigration checkpoint. Japan already has health monitoring cameras focused on approaching individuals, to determine their health condition; the here described system would also be installed in such a controlled area with restricted walk corridors of walk and controlled lighting. Gait is a remote biometrics and can provide early warning; on another hand it can be used as corroborating evidence in a multi-modal biometrics system. A database of images including shadows for a set of 28 walking people was collected, and the features extracted from shadow areas by affine moment invariants, after which identification of the subject followed. The experiments using the database show the effectiveness of the proposed method and further prove the superiority of using multiple viewpoints compared to a single viewpoint.

We discuss the potential and difficulties of using infrared (IR) spectroscopy of the human epidermis as a biometric. We present preliminary data on the fingerpads of 9 individuals demonstrating the potential for uniqueness and stability. We also present data on the challenges presented by complications such as sebum changes, intra-individual location variability, and skin care products.

This paper reports for the first time experiments on the fusion of footsteps and face on an unsupervised and not controlled environment for person authentication. Footstep recognition is a relatively new biometric based on signals extracted from people walking over floor sensors. The idea of the fusion between footsteps and face starts from the premise that in an area where footstep sensors are installed it is very simple to place a camera to capture also the face of the person that walks over the sensors. This setup may find application in scenarios like ambient assisted living, smart homes, eldercare, or security access. The paper reports a comparative assessment of both biometrics using the same database and experimental protocols. In the experimental work we consider two different applications: smart homes (small group of users with a large set of training data) and security access (larger group of users with a small set of training data) obtaining results of 0.9% and 5.8% EER respectively for the fusion of both modalities. This is a significant performance improvement compared with the results obtained by the individual systems.

Although human iris pattern is widely accepted as a stable biometric feature, recent research has found some evidences on the aging effect of iris system. In order to investigate changes in iris recognition performance due to the elapsed time between probe and gallery iris images, we examine the effect of elapsed time on iris recognition utilizing 7,628 iris images from 46 subjects with an average of ten visits acquired over two years from a legacy database at Clarkson University. Taken into consideration the impact of quality factors such as local contrast, illumination, blur and noise on iris recognition performance, regression models are built with and without quality metrics to evaluate the degradation of iris recognition performance based on time lapse factors. Our experimental results demonstrate the decrease of iris recognition performance along with increased elapsed time based on two iris recognition system (the modified Masek algorithm and a commercial software VeriEye SDK). These results also reveal the significance of quality factors in iris recognition regression indicating the variability in match scores. According to the regression analysis, our study in this paper helps provide the quantified decrease on match scores with increased elapsed time, which indicates the possibility to implement the prediction scheme for iris recognition performance based on learning of impact on time lapse factors.

The widespread use of computers throughout modern society introduces the necessity for usable and counterfeit-resistant authentication methods to ensure secure access to personal resources such as bank accounts, e-mail, and social media. Current authentication methods require tedious memorization of lengthy pass phrases, are often prone to shouldersurfing, and may be easily replicated (either by counterfeiting parts of the human body or by guessing an authentication token based on readily available information). This paper describes preliminary work toward a counterfeit-resistant usable eye movement-based (CUE) authentication method. CUE does not require any passwords (improving the memorability aspect of the authentication system), and aims to provide high resistance to spoofing and shoulder-surfing by employing the combined biometric capabilities of two behavioral biometric traits: 1) oculomotor plant characteristics (OPC) which represent the internal, non-visible, anatomical structure of the eye; 2) complex eye movement patterns (CEM) which represent the strategies employed by the brain to guide visual attention. Both OPC and CEM are extracted from the eye movement signal provided by an eye tracking system. Preliminary results indicate that the fusion of OPC and CEM traits is capable of providing a 30% reduction in authentication error when compared to the authentication accuracy of individual traits.

Vein recognition is becoming an effective method for personal recognition. Vein patterns lie under the skin surface of human body, and hence provide higher reliability than other biometric traits and hard to be damaged or faked. This paper proposes a novel vein feature representation method call orientation of local binary pattern (OLBP) which is an extension of local binary pattern (LBP). OLBP can represent the orientation information of the vein pixel which is an important characteristic of vein patterns. Moreover, the OLBP can also indicate on which side of the vein centerline the pixel locates. The OLBP feature maps are encoded by 4-bit binary values and an orientation distance is developed for efficient feature matching. Based on OLBP feature representation, we construct a hand vein recognition system employing multiple hand vein patterns include palm vein, dorsal vein, and three finger veins (index, middle, and ring finger). The experimental results on a large database demonstrate the effectiveness of the proposed approach.