The FY09 budgets for homeland security research and development programs in the U.S. are summarized. Homeland security policy developments that can influence future efforts are discussed. Initial indications of the new administration direction on homeland security R&D are summarized. An overview of the Optics and Photonics in Global Homeland Security V conference is presented.

The Department of Homeland Security (DHS) sponsored Urban Dispersion Program (UDP) resulted in the strategic placement of weather instruments in New York City (NYC) and the transition of some instruments to the City College of New York (CCNY) operated NYC MetNet to provide timely and accurate information on "skimming field" winds above city building tops. In order to extend the observational capabilities of the NYC MetNet, a cost effective portable eye safe fiber optic based coherent wind lidar system is currently under development in CCNY laboratories. Wind lidar measurements, coupled with the continuous observations from the NYC MetNet, should support the initialization, feedback and development of plume models that would be used after an initial detection of airborne toxins. An overview of the lidar system design and the NYC MetNet will be given.

Effective defense against chemical and biological threats requires an "end-to-end" strategy that encompasses the entire problem space, from threat assessment and target hardening to response planning and recovery. A key element of the strategy is the definition of appropriate system requirements for surveillance and detection of threat agents. Our end-to-end approach to venue chem/bio defense is captured in the Facilities Weapons of Mass Destruction Decision Analysis Capability (FacDAC), an integrated system-of-systems toolset that can be used to generate requirements across all stages of detector development. For example, in the early stage of detector development the approach can be used to develop performance targets (e.g., sensitivity, selectivity, false positive rate) to provide guidance on what technologies to pursue. In the development phase, after a detector technology has been selected, the approach can aid in determining performance trade-offs and down-selection of competing technologies. During the application stage, the approach can be employed to design optimal defensive architectures that make the best use of available technology to maximize system performance. This presentation will discuss the end-to-end approach to defining detector requirements and demonstrate the capabilities of the FacDAC toolset using examples from a number of studies for the Department of Homeland Security.

Objective: The objective of this study is to determine if LC/MS/MS based isotopic tagging technique is useful to detect putative breast cancer markers in saliva. Methods: Six pooled (n=10 subjects per pool) stimulated whole saliva specimens from women were analyzed. One pooled specimen was from healthy women, another pooled specimen from women diagnosed with a benign breast tumor and the other four-pooled specimens were from women diagnosed with carcinoma of the breast. The four cancer specimens were staged 0, I, IIa and IIb (lymph node involvement). Isotopically tagging proteins in the tumor groups and comparing them to the healthy control group measured differential expression of proteins. The iTRAQ labels are isobaric and chemically identical, thus labeled peptides from the different pools have identical elution times and masses. The relative concentration of identified proteins in each of the mixtures is determined from the intensities of each of the reporter ions. Results: The results of the salivary analyses yielded approximately 209 proteins in the saliva specimens. These proteins were able to distinguish between the healthy control, the benign and the cancer patients. Additionally, there were proteins able to distinguish between node positive and node negative patients.

The practical utility of technologies for early detection of human exposure to a variety of toxic agents has been limited in many cases by the absence of instruments suitable for first responders and at field hospitals. Microarrays provide multiplexed assay of a large number of human biomarkers, including cytokines and chemokines, indicators of immune system health. Assay of saliva is less invasive and provides quick indication of exposure especially of the respiratory system. Our pilot clinical study has uncovered an early cytokine response in human saliva. As a model for respiratory exposure, a cohort of 16 adult volunteers was challenged with FluMist^TM vaccinations, an FDA approved, attenuated live influenza virus. Blood and saliva cytokine levels were monitored immediately prior to and up to 7 days afterwards. Bead assay found little change in blood cytokine levels while several of those in saliva were frequently elevated above two standard deviations on trial days one and three. We have developed a prototype portable saliva monitoring system consisting of microarray cytokine capture plate, luminescent reporter, and whole plate imaging. Assay is with a commercial 96-well plate spotted with up to 16 distinct biomarkers per well and read by chemiluminescence. A battery-powered, 16-bit, cooled-CCD camera and laptop PC provide imaging and data reduction. Detection limits of common inflammatory cytokines were measured at about 1-5 pg/ml which is within the clinically significant range for saliva of exposed individuals, as verified for samples from the small clinical trial. An expanded study of cytokine response in saliva of therapeutic radiation oncology patients is being launched.

A comprehensive resequencing microarray "Tropical and Emerging Infections (TessArray RPM-TEI 1.0 array)" has been developed to identify and distinguish between biothreat organisms of interest and genetically close related species. This array has undergone validation using an innovative approach where synthetic DNA fragments are used for organisms that it is not safe to work with outside a biosafety 3 facilities. The approach was confirmed from testing a subset of target organisms, such as Ebola viruses and Lassa viruses, at USAMRIID. Most potential biothreat organisms are actually endemic in some part of the world. Proper surveillance of biothreat agents will require some form of monitoring the evolution of the indigenous organisms under their natural environment, so when changes in the organisms occur, the diagnostic assays for these organisms can be reviewed to assure they still provide detection. Using the resequencing microarray (RPM) for detection in locations such as the Africa can support indigenous monitoring as it provides sequence information. An ongoing collaboration with Njala University aims to establish a broad-spectrum pathogen surveillance capability in the Republic of Sierra Leone, West Africa using RPM technology combined with a Geographic Information System. This has the potential to improve the public health efforts in an infected area as well as provide monitoring of the changes occurring to a biothreat organism, i.e. Lassa viruses, in its natural location.

Biophotonic imaging is a versatile and powerful tool, that when combined with living microbial bioreporters, can be applied in diagnostic technologies for sensitive, nondestructive, real-time monitoring of chemical and biological targets. Bioreporters, consisting of bacteria as well as the viruses (bacteriophage) that infect them, can be genetically engineered to emit visible light upon interaction with a specific chemical or biological entity. By interfacing these bioreporters with imaging cameras or miniaturized integrated circuit microluminometers, fully standalone detection units are formed that can be deployed for intelligent distributed multi-target chem/bio monitoring.

Porphyrin-embedded materials (PEMs) combine the tunable binding selectivity, high surface area, and low materials density of a highly ordered pore network with the unique properties of porphyrins. Porphyrins are a family of large, nearly planar molecules which strongly absorb visible light and fluoresce intensely. They are highly sensitively to alterations in their immediate environment making porphyrins valuable as indicators. They are well established electroand photocatalysts and have been used in this capacity in a wide range of applications. Surfactant-templated nanoporous organosilicas have great potential for the adsorption of small molecule contaminants. They can be synthesized with high surface areas, uniform pore dimensions, and ordered nanostructures while incorporating organic bridging groups in the pore walls that may be tuned for adsorption of a specific class of compounds. In the porphyrin-embedded materials, the organosilica scaffold stabilizes the porphyrin and facilitates optimal orientation of porphyrin and target. The materials can be stored under ambient conditions and offer exceptional shelf-life. The selectivity of the materials can be controlled both through incorporation of varying organic bridging groups in the organosilica structure and through selection of the porphyrin component.

Hanson Technologies has developed the automated OmniFresh 1000 system to sample large volumes of produce wash water, collect the pathogens, and detect their presence. By collecting a continuous sidestream of wash water, the OmniFresh uses a sample that represent the entire lot of produce being washed. The OmniFresh does not require bacterial culture or enrichment, and it detects both live and dead bacteria in the collected sample using an in-line sensor. Detection occurs in an array biosensor capable of handling large samples with complex matrices. Additionally, sample can be sent for traditional confirming tests after the screening performed by the OmniFresh.

Emergency Diagnostics, Homeland Security, Epidemiological Preparedness and the high cost of the Health Care Systems have increased demand for affordable and mobile point of care (POC) devices with highest sensitivity, specificity and rapid time to result. We have developed pocket and brief case sized systems for point of care and field based tests based on fluorescence read-out. The core consists of battery operated, 90 gram electro-optical units with optional wireless data transfer, which have been optimized to achieve highest accuracy and sensitivity combined with simplicity of use. The robust systems have been applied to molecular diagnostics such as DNA based testing, immunodiagnostics as well as environmental monitoring and agricultural testing. Starting with the current bottlenecks of in-vitro diagnostics testing and a brief market overview, we will show commercially available portable test systems for molecular diagnostics and how we solve the current bottlenecks. We will further show battery operated handheld prototypes for DNA testing. ESE's handheld and portable testing platforms have been shown to provide sensitive, accurate, and specific results, as well as rapid turnaround. The stand-alone devices demonstrate operational and physical robustness, and they can be manufactured to be affordable.

The potential for the use of biological agents by terrorists is a real threat. Two approaches for antibody-based detection of biological species are described in this paper: 1) The use of microbead arrays for multiplexed flow cytometry detection of cytokines and botulinum neurotoxin simulant, and 2) a microfluidic platform for capture and separation of different size superparamagnetic nanoparticles followed by on-chip fluorescence detection of the sandwich complex. These approaches both involve the use of automated fluidic systems for trapping antibody-functionalized microbeads, which allows sample, assay reagents, and wash solutions to be perfused over a micro-column of beads, resulting in faster and more sensitive immunoassays. The automated fluidic approach resulted in up to five-fold improvements in immunoassay sensitivity/speed as compared to identical immunoassays performed in a typical manual batch mode. A second approach for implementing multiplexed bead-based immunoassays without using flow cytometry detection is currently under development. The goal of the microfluidic-based approach is to achieve rapid (<20 minutes), multiplexed (≥ 3 bioagents) detection using a simple and low-cost, integrated microfluidic/optical detection platform. Using fiber-optic guided laser-induced fluorescence, assay detection limits were shown to be in the 100's of picomolar range (10's of micrograms per liter) for botulinum neurotoxin simulant without any optimization of the microfluidic device or optical detection approach.

Future viral outbreaks are a major threat to societal and economic development throughout the world. A rapid, sensitive, and easy-to-use test for viral infections is essential to prevent and to control such viral pandemics. Furthermore, a compact, portable device is potentially very useful in remote or developing regions without easy access to sophisticated laboratory facilities. We have developed a rapid, ultrasensitive sensor that could be used in a handheld device to detect various viruses and measure their concentration. The essential innovation in this technique is the combination of an integrated optical interferometric sensor with antibody-antigen recognition approaches to yield a very sensitive, very rapid test for virus detection. The sensor is able to spot the herpes virus at concentrations of just 850 particles per milliliter under physiological conditions. The sensitivity of the sensor approaches detection of a single virus particle, yielding a sensor of unprecedented sensitivity with wide applications for viral diagnostics. The sensor's detection principle can be extended to any biological target such as bacteria, cells and proteins and for which there are specific antibodies. The nature of the sensor enables multiplexed detection of several analytes at the same time.

DNA microarrays are becoming a widespread tool used in life science and drug screening due to its many benefits of miniaturization and integration. Microarrays permit a highly multiplexed DNA analysis. Recently, the development of new detection methods and simplified methodologies has rapidly expanded the use of microarray technologies from predominantly gene expression analysis into the arena of diagnostics. Osmetech's eSensor^® is an electrochemical detection platform based on a low-to- medium density DNA hybridization array on a cost-effective printed circuit board substrate. eSensor^® has been cleared by FDA for Warfarin sensitivity test and Cystic Fibrosis Carrier Detection. Other genetic-based diagnostic and infectious disease detection tests are under development. The eSensor^® platform eliminates the need for an expensive laser-based optical system and fluorescent reagents. It allows one to perform hybridization and detection in a single and small instrument without any fluidic processing and handling. Furthermore, the eSensor^® platform is readily adaptable to on-chip sample-to-answer genetic analyses using microfluidics technology. The eSensor^® platform provides a cost-effective solution to direct sample-to-answer genetic analysis, and thus have a potential impact in the fields of point-of-care genetic analysis, environmental testing, and biological warfare agent detection.

Excerpt from the US Transportation Security Agency website: "The ban on liquids, aerosols and gels was implemented on August 10 after a terrorist plot was foiled. Since then, experts from around the government, including the FBI and our national labs have analyzed the information we now have and have conducted extensive explosives testing to get a better understanding of this specific threat." In order to lift the ban and ease the burden on the flying public, Reveal began an extensive effort in close collaboration with the US and several other governments to help identify these threats. This effort resulted in the successful development and testing of an automated explosive detection system capable of resolving these threats with a high probability of detection and a low false alarm rate. We will present here some of the methodology and approach we took to address this problem.

As an alternative to focusing nanosecond pulses for stand-off LIBS detection of energetic materials, we use self-channeled femtosecond pulses from a Ti:Sapphire laser to produce filaments at 12 meters and create a plasma on copper, graphite and polyisobutylene film. We show the possibilities of this Laser-Induced Breakdown Spectroscopy configuration for thin organic sample detection on a surface at a distance.

The objective of this accomplished project funded by the German BMBF was to develop a single photon ionization ion trap mass spectrometer (SPI-ITMS) for detection of security relevant substances in complex matrices at low concentrations. The advantage of such a soft ionization technique is a reduction of target ion fragmentation allowing identification of signals from complex matrices and enabling MS/MS capability. To obtain low detection limits, the applied photon energy has to be below the ionization potential (IP) of the bulk matrix components. Therefore, photon energies between 8 eV (155 nm) and 12 eV (103 nm) are necessary which was achieved with newly developed electron beam excimer lamps (EBEL). They generate light at different wavelengths depending on the selected rare gas emitting wavelengths adapted to the analyzed substances. So, e.g. with a krypton-EBEL with 8.4 eV photon energy most narcotics can be ionized without notable fragmentation. Due to their higher IPs, EBEL with higher photon energy have to be used for most explosives. Very low false-positive and false-negative rates have been achieved using MS/MS studies. First field tests of a demonstrator provided the proof of principle.

Terahertz and millimeter-wave imaging technologies, wherever they are applied to human beings, generate problems with the "naked" body. Security issues thus inevitably lead to ethical questions of privacy and intimacy. Less apparent but no less important are other issues such as discrimination and the question of reducing this problem through post processing of data; scalability; questions of controlling the controllers; questions of proliferation. Ethical research alone can not provide acceptability. However, ultimately innovative technologies will not achieve widespread and sustainable acceptance without a fundamental clarification of the ethically relevant issues.

The GE Intelligent Personal Radiation Locator (IPRL) system consists of multiple hand held radiation detectors and a base station. Each mobile unit has a CZT Compton camera radiation detector and can identify isotopes and determine the direction from which the radiation is detected. Using GPS and internal orientation sensors, the system continuously transforms all directional data into real-world coordinates. Detected radiation is wirelessly transmitted to the base station for system-wide analysis and situational awareness. Data can also be exchanged wirelessly between peers to enhance the overall detection efficiency of the system. The key design features and performance characteristics of the GE IPRL system are described.

Colloidal synthesis of core/shell nanocrystals with cerium-doped lanthanum fluoride core and undoped lanthanum fluoride shell, and of core/shell nanocrystals with hygroscopic cerium-doped lanthanum bromide core and undoped lanthanum fluoride shell is reported. The nanocrystals were characterized by transmission electron microscopy (TEM), energy dispersive X-ray spectroscopy (EDS), dynamic light scattering (DLS) analysis, steady state UV-VIS optical absorption and photoluminescence spectroscopy, and by photoluminescence lifetime measurements. Scintillation tests were performed on the cerium-doped lanthanum fluoride nanocrystalline material exposed to low-level gamma irradiation.

For many years, fiber manufacturers have devoted research efforts to develop fibers with improved radiation resistance, keeping the same advantages and basic properties as standard fibers. Today, both single-mode (SMF) and multimode (MMF) RadHard (for Radiation-Hardened) fibers are available; some of them are MIL-49291 certified and are already used, for example in military applications and at the Large Hadron Collider (LHC) in CERN or in certain nuclear power plants. These RadHard fibers can be easily connected to standard optical networks for classical data transfer or they can also be used for command control. Using some specific properties (Raman or Brillouin scattering, Bragg gratings...), such fibers can also be used as distributed sensing (temperature or strain sensors, etc) in radiation environments. At least, optical fibers can also be used for signal amplification, either in telecom networks, or in fiber lasers. This last category of fibers is called active fibers, in opposition to passive fibers used for simple signal transmission. Draka has also recently worked to improve the radiation-resistance of these active fibers, so that Draka can now offer RadHard fibers for full optical systems.

X-ray diffraction imaging (XDI) is a novel modality in which the local x-ray diffraction (XRD) properties of inhomogenous objects are measured. Following a brief description of some of the areas in which x-ray diffraction is currently impacting on the detection of materials of interest in the security environment, the principles of energy-dispersive x-ray diffraction tomography employed in XDI are described. The Multi-Inverse Fan Beam (MIFB) topology for 3rd Generation XDI, in which the XRD properties of a spatial array of 2-D volume elements are investigated in parallel without mechanical scanning, is described. 3rd Generation XDI is being driven among other things by rapid technological developments taking place in the field of spectroscopic, room-temperature, semiconductor x-ray detectors. Detector requirements for Next-Generation MIFB XDI are summarized and the potential of 3rd Generation XDI for rapid, accurate and affordable screening in the Checkpoint and Hold Baggage environments is summarized.

Light scattering is one of the most fundamental optical processes whereby electromagnetic waves are forced to deviate from a straight trajectory by non-uniformities in the medium that they traverse. This presentation summarizes our recent research on application of light-scatter measurements paired with machine learning and pattern recognition methodologies for label-free classification of bioparticles. Two separate examples of light scatter-based techniques are discussed: forward-scatter measurements of bacterial colonies in an imaging system, and flow cytometry measurements of scatter signals formed by individual bacterial particles. Recently, we have reported a first practical implementation of a system capable of label-free classification and recognition of pathogenic species of Listeria, Salmonella, Vibrio, Staphylococcus, and E. coli using forward-scatter patterns produced by bacterial colonies irradiated with laser light. Individual bacteria in flow also form complex patterns dependent on particle size, shape, refraction index, density, and morphology. Although commercial flow cytometers allow scatter measurement at two angles this rudimentary approach cannot be used to separate populations of bioparticles of similar shape, size, or structure. The custom-built system used in the presented work collects axial light-loss and scatter signals at five carefully chosen angles. Experimental results obtained from colony scanner, as well from the extended cytometry instrument, were used to train the pattern-recognition algorithm. The results demonstrate that information provided by scatter alone may be sufficient to recognize various bioparticles with 90-99% success rate, both in flow and in imaging systems.

We have recently developed an elliptically polarized light scattering (EPLS) technique to characterize the morphology of fine particles suspended in an optically non-absorbing medium such as water. This technique provides the size distribution, shape and agglomeration characteristics of suspended particles. This technique can be used to detect various types of biological pathogens such as bacteria, protozoa and viruses in potable water systems. Here we report results obtained from EPLS measurements on two strains of Bacillus spores suspended in water along with comparison with electron microscopy.

BlazeTech has recently developed a software called H₂OFate to predict the fate and transport of chemical and biological contaminants in water distribution systems. This software includes models for the reactions of these contaminants with residual disinfectant in bulk water and at the pipe wall, and their adhesion/reactions with the pipe walls. This software can be interfaced with sensors through SCADA systems to monitor water distribution networks for contamination events and activate countermeasures, as needed. This paper presents results from parametric calculations carried out using H2OFate for a simulated contaminant release into a sample water distribution network.

Water infrastructure needs in the US are expected to exceed a cost of over 300 billion dollars in the coming years. While security has become a priority since 9/11 budgets for this expenditure are often constrained. This necessitates that solutions be dual use in nature. Since 9/11 numerous communities have installed multi-parameter monitoring stations in the distribution system as early warning systems for potential water security threats. These systems have recorded large streams of data relevant to water quality in the distribution systems. In this study data streams from a number of communities are analyzed for pertinent information as to the health and operation of the distribution system. Changes in water quality are correlated with known causes attributable to day-to-day operational changes and also anomalous events (pipe bursts, accidental back flows, cross connections, chemical over feeds, treatment plant problems, nitrification events, etc.). Information concerning what action was taken to ameliorate the problem will also be linked to the data for the identified events thus demonstrating dual use for these systems.

The paper pertains to the reduction of measurement errors in gyroscopes used for tracking the position of walking persons. Some of these tracking systems commonly use inertial or other means to measure distance traveled, and one or more gyros to measure changes in heading. MEMS-type gyros or IMUs are best suited for this task because of their small size and low weight. However, these gyros have large drift rates and can be sensitive to accelerations. The Heuristic Drift Reduction (HDR) method presented in this paper estimates the drift component and eliminates it, reducing heading errors by almost one order of magnitude.

In order to demonstrate the possibility of identifying the material within ship's underwater hull, sunken ships and other objects on the sea floor tests with the 14 MeV sealed tube neutron generator incorporated inside a small submarine submerged in the test basin filled with sea water have been performed. Results obtained for inspection of diesel fuel and explosive presence behind single and double hull constructions are presented.

While polluted sediments present a threat to the health of the marine ecosystem and indirectly to the public health, ammunition dump sites being mostly unprotected and neglected, present a serious threat to human security, environmental security and could be possible objects of misuse. Of special interest are sediments in ports and marinas. Those are the places where any suspicious object needs to be analyzed for the presence of explosives and CW. After analyzing several hundreds of sediment samples collected along the Adriatic coast, it has been found that they could be grouped in 7 categories: bays, beaches, villages, ports, marinas - pier area, marina - service areas and others. We have shown that the sediments in ports and harbors contain increased values of elements present in antifouling paints (Cu, As, Zn and Pb). Their presence modifies the response of survey probes while screening the sea floor for the presence of explosives and CW.

To protect naval and commercial ships from attack by terrorists and pirates, it is important to have automatic surveillance systems able to detect, identify, track and alert the crew on small watercrafts that might pursue malicious intentions, while ruling out non-threat entities. Radar systems have limitations on the minimum detectable range and lack high-level classification power. In this paper, we present an innovative Automated Intelligent Video Surveillance System for Ships (AIVS3) as a vision-based solution for ship security. Capitalizing on advanced computer vision algorithms and practical machine learning methodologies, the developed AIVS3 is not only capable of efficiently and robustly detecting, classifying, and tracking various maritime targets, but also able to fuse heterogeneous target information to interpret scene activities, associate targets with levels of threat, and issue the corresponding alerts/recommendations to the man-in- the-loop (MITL). AIVS3 has been tested in various maritime scenarios and shown accurate and effective threat detection performance. By reducing the reliance on human eyes to monitor cluttered scenes, AIVS3 will save the manpower while increasing the accuracy in detection and identification of asymmetric attacks for ship protection.

In this work, we present research performed to improve the receiver characteristics for underwater imaging applications using the hybrid lidar-radar detection technique. We report the development of the next-generation coherent heterodyne receiver using modulation of the optical receiver's amplifier gain. Significant advantages in the receiver specifications are achieved using a large-area, high gain, low-noise silicon avalanche photodiode (APD) as the photodetector cum frequency mixer-demodulator. We demonstrate that heterodyne detection by gain modulation of APD can be used to increase the signal-to-noise ratio, detection sensitivity and bandwidth for the hybrid receiver system.

Development of microfluidics has focused on carrying out chemical synthesis and analysis in ever-smaller volumes of solution. In most cases, flow systems are made of either quartz, glass, or an easily moldable polymer such as polydimethylsiloxane (Whitesides 2006). As the system shrinks, the ratio of surface area to volume increases. For studies of either free radical chemistry or protein chemistry, this is undesirable. Proteins stick to surfaces, biofilms grow on surfaces, and radicals annihilate on walls (Lewis et al. 2006). Thus, under those circumstances where small amounts of reactants must be employed, typical microfluidic systems are incompatible with the chemistry one wishes to study. We have developed an alternative approach. We use ultrasonically levitated microliter drops as well mixed microreactors. Depending on whether capillaries (to form the drop) and electrochemical sensors are in contact with the drop or whether there are no contacting solids, the ratio of solid surface area to volume is low or zero. The only interface seen by reactants is a liquid/air interface (or, more generally, liquid/gas, as any gas may be used to support the drop). While drop levitation has been reported since at least the 1940's, we are the second group to carry out enzyme reactions in levitated drops, (Weis; Nardozzi 2005) and have fabricated the lowest power levitator in the literature (Field; Scheeline 2007). The low consumption aspects of ordinary microfluidics combine with a contact-free determination cell (the levitated drop) that ensures against cross-contamination, minimizes the likelihood of biofilm formation, and is robust to changes in temperature and humidity (Lide 1992). We report kinetics measurements in levitated drops and explain how outgrowths of these accomplishments will lead to portable chemistry/biology laboratories well suited to detection of a wide range of chemical and biological agents in the asymmetric battlefield environment.

Automatic face recognition is a challenging task due to intra-class variations. Changes in lighting conditions during enrolment and identification stages contribute significantly to these intra-class variations. A common approach to address the effects such of varying conditions is to pre-process the biometric samples in order normalise intra-class variations. Histogram equalisation is a widely used illumination normalisation technique in face recognition. However, a recent study has shown that applying histogram equalisation on well-lit face images could lead to a decrease in recognition accuracy. This paper presents a dynamic approach to illumination normalisation, based on face image quality. The quality of a given face image is measured in terms of its luminance distortion by comparing this image against a known reference face image. Histogram equalisation is applied to a probe image if its luminance distortion is higher than a predefined threshold. We tested the proposed adaptive illumination normalisation method on the widely used Extended Yale Face Database B. Identification results demonstrate that our adaptive normalisation produces better identification accuracy compared to the conventional approach where every image is normalised, irrespective of the lighting condition they were acquired.

This paper proposes a new discrete particle swarm optimization (DPSO) algorithm with a multiplicative likeliness enhancement rule for unordered feature selection. In this paper, the pool of features for face recognition are derived from direct fractional-step linear discriminant analysis (DFLDA). Each particle is associated with a subset of features, and their recognition performance on the validation set influences the particle's fitness with randomness. Features are selected by their assigned likeliness, which is enhanced by the agreement between a particle and its attractors (its previous location, pbest and gbest). The new DPSO double-asserts or triple-asserts the selection if the attractors share common features. The feature selection technique proposed in this paper is a modular procedure and thus can be applied to other features if a separate validation set is available for fitness evaluation. This DPSO algorithm is successfully applied on the FERET database. The recognition performance is improved for both L1 and L2 norm distance metrics. The cumulative matching score (CMS) is improved for higher ranks, which indicates that this performance improvement is beneficial for identification task. In overall comparison, the multiplicative updating rule achieves higher fitness and smaller standard deviation than the additive likeliness enhancement rule.

It is both crucial and challenging to protect biometric data used for biometric identification and authentication systems, while keeping the systems user friendly. We study the design and analysis of biometric data protection schemes based on fuzzy extractors. There are limitations in previous fuzzy extractors, which make them difficult to handle continuous feature spaces, entropy estimation, and feature selection. We proposed a scheme based on PCA features and a recently proposed fuzzy extractor for continuous domains. We conduct experiments using the ORL face database, and analyze carefully the entropies and the resulting security of the system. We explore and compare different ways to select and combine features, and show that randomization plays an important role in both security, performance and cancelability. Furthermore, proposed feature selection does yield better estimation of the final key strength.

Many security applications require accurate identification of people, and research has shown that iris biometrics can be a powerful identification tool. However, in order for iris biometrics to be used on larger populations, error rates in the iris biometrics algorithms must be as low as possible. Furthermore, these algorithms need to be tested in a number of different environments and configurations. In order to facilitate such testing, we have collected more than 100,000 iris images for use in iris biometrics research. Using this data, we have developed a number of techniques for improving recognition rates. These techniques include fragile bit masking, signal-level fusion of iris images, and detecting local distortions in iris texture. Additionally, we have shown that large degrees of dilation and long lapses of time between image acquisitions negatively impact performance.

The ear has gained popularity as a biometric feature due to the robustness of the shape over time and across emotional expression. Popular methods of ear biometrics analyze the ear as a whole, leaving these methods vulnerable to error due to occlusion. Many researchers explore ear recognition using an ensemble, but none present a method for designing the individual parts that comprise the ensemble. In this work, we introduce a method of modifying the ensemble shapes to improve performance. We determine how different properties of an ensemble training system can affect overall performance. We show that ensembles built from small parts will outperform ensembles built with larger parts, and that incorporating a large number of parts improves the performance of the ensemble.

Ear biometrics has been found to be a good and reliable technique for human recognition. With the initial doubts on uniqueness of the ear, ear biometrics could not attract much attention. But after it has been said that it is almost impossible to find two ears with all the parts identical, ear biometrics has gained its pace. To automate the ear based recognition process, ear in the image is required to be localized automatically. This paper presents a technique for the same. Ear localization in the proposed technique is carried out by using the hierarchical clustering of the edges obtained from the side face image. The technique is tested on a database consisting of 500 side face images of human faces collected at IIT Kanpur. It is found to be giving 94.6% accuracy.

Iris masks are essential in iris recognition. The purpose of having a good iris mask is to indicate which part of iris texture map is useful and which part is occluded or contains noisy artifacts such as eyelashes, eyelids and specular reflections. The accuracy of the iris mask is extremely important. The performance of the iris recognition system will decrease dramatically when iris mask is inaccurate, even when the best recognition algorithm is used. Traditionally, people used naive rule-based algorithms to estimate iris masks from the iris texture map. But the accuracy of the iris mask generated in this way is questionable. In this paper, we propose a probabilistic and learning-based method to automatically estimate iris mask from iris texture map. The features used in this method are very simple, yet the resulting estimated iris mask is significantly more accurate than the rule-based methods. We also demonstrate the effectiveness of the algorithm by performing iris recognition based on masks estimated by different algorithms. Experimental results show the masks estimated by the proposed algorithm help to increase the iris recognition rate on NIST Iris Challenge Evaluation (ICE) database.

Iris recognition utilizes distinct patterns found in the human iris to perform identification. Image acquisition is a critical first step towards successful operation of iris recognition systems. However, the quality of iris images required by standard iris recognition algorithms puts hard constraints on the imaging optical systems which have resulted in demonstrated systems to date requiring a relatively short subject stand-off distance. In this paper, we study long-range iris recognition at distances as large as 200 meters, and determine conditions the imaging system must satisfy for identification at longer stand-off distances.

A novel approach using mechanical physiological activity as a biometric marker is described. Laser Doppler Vibrometry is used to sense activity in the region of the carotid artery, related to arterial wall movements associated with the central blood pressure pulse. The non-contact basis of the LDV method has several potential benefits in terms of the associated non-intrusiveness. Several methods are proposed that use the temporal and/or spectral information in the signal to assess biometric performance both on an intra-session basis, and on an intersession basis involving testing repeated after delays of 1 week to 6 months. A waveform decomposition method that utilizes principal component analysis is used to model the signal in the time domain. Authentication testing for this approach produces an equal-error rate of 0.5% for intra-session testing. However, performance degrades substantially for inter-session testing, requiring a more robust approach to modeling. Improved performance is obtained using techniques based on time-frequency decomposition, incorporating a method for extracting informative components. Biometric fusion methods including data fusion and information fusion are applied in multi-session data training model. As currently implemented, this approach yields an inter-session equal-error rate of 9%.

Small movements of the skin overlying the carotid artery, arising from pressure pulse changes in the carotid during the cardiac cycle, can be detected using the method of Laser Doppler Vibrometry (LDV). Based on the premise that there is a high degree of individuality in cardiovascular function, the pulse-related movements were modeled for biometric use. Short time variations in the signal due to physiological factors are described and these variations are shown to be informative for identity verification and recognition. Hidden Markov models (HMMs) are used to exploit the dependence between the pulse signals over successive cardiac cycles. The resulting biometric classification performance confirms that the LDV signal contains information that is unique to the individual.

Biometrics, such as fingerprint, iris scan, and face recognition, offer methods for identifying individuals based on a unique physiological measurement. Recent studies indicate that a person's electrocardiogram (ECG) may also provide a unique biometric signature. Current techniques for identification using ECG rely on empirical methods for extracting features from the ECG signal. This paper presents an alternative approach based on a time-domain model of the ECG trace. Because Auto-Regressive Integrated Moving Average (ARIMA) models form a rich class of descriptors for representing the structure of periodic time series data, they are well-suited to characterizing the ECG signal. We present a method for modeling the ECG, extracting features from the model representation, and identifying individuals using these features.

We present three-dimensional imaging of artificial fingerprints using the Digital Interference Holography (DIH) scanner. DIH is based on a multiwavelength optical sensing technique that can be used to build holographically the three dimensional structure of the fingerprints. Many holograms (~50) were acquired by a CCD camera by scanning a range of wavelengths. Each hologram was numerically reconstructed and then superposed yielding tomographic images which represented the artificial fingerprint structure. The axial resolution is a parameter that depends on the wavelength scanning range and is about 5 μm. The light source was a solid state pumped dye laser with a tunable wavelength range of 550 nm to 600 nm. Holograms were captured by a monochrome CCD camera (Sony XC-ST50, with 780 × 640 pixels and a pixel size of ~ 9 μm). An image acquisition board (NI IMAQ PCI-1407) digitized the image with 8 bit resolution. All software was developed in house with the NI LabView. We used a Michelson interferometer in a backscattering geometry and the reconstruction of the optical field was done using the angular spectrum algorithm. Our goal is to identify and quantify, Level 1 (pattern), Level 2 (minutia points), and Level 3 (ridge contours) features from the amplitude images, using the DIH technique and fingerprints recognition. The results could be used in the two fingerprint matching phases, identification and verification.

Radar has established itself as an effective all-weather, day or night sensor. Radar signals can penetrate walls and provide information on moving targets. Recently, radar has been used as an effective biometric sensor for classification of gait. The return from a coherent radar system contains a frequency offset in the carrier frequency, known as the Doppler Effect. The movements of arms and legs give rise to micro Doppler which can be clearly detailed in the time-frequency domain using traditional or modern time-frequency signal representation. In this paper we propose a gait classifier based on subspace learning using principal components analysis(PCA). The training set consists of feature vectors defined as either time or frequency snapshots taken from the spectrogram of radar backscatter. We show that gait signature is captured effectively in feature vectors. Feature vectors are then used in training a minimum distance classifier based on Mahalanobis distance metric. Results show that gait classification with high accuracy and short observation window is achievable using the proposed classifier.

A new method to generate synthetic online signatures is presented. The algorithm uses a parametrical model to generate the synthetic Discrete Fourier Transform (DFT) of the trajectory signals, which are then refined in the time domain and completed with a synthetic pressure function. Multiple samples of each signature are created so that synthetic databases may be produced. Quantitative and qualitative results are reported, showing that, in addition to presenting a very realistic appearance, the synthetically generated signatures have very similar characteristics to those that enable the recognition of real signatures.

Today there is no direct path for the conversion of a floating-point algorithm implementation to an optimized fixed-point implementation. This paper proposes a novel and efficient methodology for Floating-point to Fixed-point Conversion (FFC) of biometric Fingerprint Algorithm Library (FAL) on fixed-point DaVinci processor. A general FFC research task is streamlined along smaller tasks which can be accomplished with lower effort and higher certainty. Formally specified in this paper is the optimization target in FFC, to preserve floating-point accuracy and to reduce execution time, while preserving the majority of algorithm code base. A comprehensive eight point strategy is formulated to achieve that target. Both local (focused on the most time consuming routines) and global optimization flow (to optimize across multiple routines) are used. Characteristic phases in the FFC activity are presented using data from employing the proposed FFC methodology to FAL, starting with target optimization specification, to speed optimization breakthroughs, finalized with validation of FAL accuracy after the execution time optimization. FAL implementation resulted in biometric verification time reduction for over a factor of 5, with negligible impact on accuracy. Any algorithm developer facing the task of implementing his floating-point algorithm on DaVinci DSP is expected to benefit from this presentation.