Cross-Spectral Full and Partial Face Recognition: Preprocessing, Feature Extraction and Matching

Cross-spectral face recognition remains a challenge in the area of biometrics. The problem arises from some real-world application scenarios such as surveillance at night time or in harsh environments, where traditional face recognition techniques are not suitable or limited due to usage of imagery obtained in the visible light spectrum. This motivates the study conducted in the dissertation which focuses on matching infrared facial images against visible light images. The study outspreads from aspects of face recognition such as preprocessing to feature extraction and to matching.;We address the problem of cross-spectral face recognition by proposing several new operators and algorithms based on advanced concepts such as composite operators, multi-level data fusion, image quality parity, and levels of measurement. To be specific, we experiment and fuse several popular individual operators to construct a higher-performed compound operator named GWLH which exhibits complementary advantages of involved individual operators. We also combine a Gaussian function with LBP, generalized LBP, WLD and/or HOG and modify them into multi-lobe operators with smoothed neighborhood to have a new type of operators named Composite Multi-Lobe Descriptors. We further design a novel operator termed Gabor Multi-Levels of Measurement based on the theory of levels of measurements, which benefits from taking into consideration the complementary edge and feature information at different levels of measurements.;The issue of image quality disparity is also studied in the dissertation due to its common occurrence in cross-spectral face recognition tasks. By bringing the quality of heterogeneous imagery closer to each other, we successfully achieve an improvement in the recognition performance. We further study the problem of cross-spectral recognition using partial face since it is also a common problem in practical usage. We begin with matching heterogeneous periocular regions and generalize the topic by considering all three facial regions defined in both a characteristic way and a mixture way.;In the experiments we employ datasets which include all the sub-bands within the infrared spectrum: near-infrared, short-wave infrared, mid-wave infrared, and long-wave infrared. Different standoff distances varying from short to intermediate and long are considered too. Our methods are compared with other popular or state-of-the-art methods and are proven to be advantageous

Advanced Feature Learning and Representation in Image Processing for Anomaly Detection

Techniques for improving the information quality present in imagery for feature extraction are proposed in this thesis. Specifically, two methods are presented: soft feature extraction and improved Evolution-COnstructed (iECO) features. Soft features comprise the extraction of image-space knowledge by performing a per-pixel weighting based on an importance map. Through soft features, one is able to extract features relevant to identifying a given object versus its background. Next, the iECO features framework is presented. The iECO features framework uses evolutionary computation algorithms to learn an optimal series of image transforms, specific to a given feature descriptor, to best extract discriminative information. That is, a composition of image transforms are learned from training data to present a given feature descriptor with the best opportunity to extract its information for the application at hand. The proposed techniques are applied to an automatic explosive hazard detection application and significant results are achieved

Midwave Infrared Imaging Fourier Transform Spectrometry of Combustion Plumes

A midwave infrared (MWIR) imaging Fourier transform spectrometer (IFTS) was used to successfully capture and analyze hyperspectral imagery of combustion plumes. Jet engine exhaust data from a small turbojet engine burning diesel fuel at a flow rate of 300 cm3/min was collected at 1 cm−1 resolution from a side-plume vantage point on a 200x64 pixel window at a range of 11.2 meters. Spectral features of water, CO, and CO2 were present, and showed spatial variability within the plume structure. An array of thermocouple probes was positioned within the plume to aid in temperature analysis. A single-temperature plume model was implemented to obtain spatiallyvarying temperatures and plume concentrations. Model-fitted temperatures of 811 ± 1.5 K and 543 ± 1.6 K were obtained from plume regions in close proximity to thermocouple probes measuring temperatures of 719 K and 522 K, respectively. Industrial smokestack plume data from a coal-burning stack collected at 0.25 cm−1 resolution at a range of 600 meters featured strong emission from NO, CO, CO2, SO2, and HCl in the spectral region 1800-3000 cm−1. A simplified radiative transfer model was employed to derive temperature and concentrations for clustered regions of the 128x64 pixel scene, with corresponding statistical error bounds. The hottest region (closest to stack centerline) was 401 ± 0.36 K, compared to an in-stack measurement of 406 K, and model-derived concentration values of NO, CO2, and SO2 were 140 ± 1 ppmV, 110,400 ± 950 ppmV, and 382 ± 4 ppmV compared to in-stack measurements of 120 ppmV (NOχ), 94,000 ppmV, and 382 ppmV, respectively. In-stack measurements of CO and HCl were not provided by the stack operator, but model-derived values of 19 ± 0.2 ppmV and 111 ± 1 ppmV are reported near stack centerline. A deployment to Dugway Proving Grounds, UT to collect hyperspectral imagery of chemical and biological threat agent simulants resulted in weak spectral signatures from several species. Plume detection of methyl salicilate was achieved from both a stack release and explosive detonation, although spectral identification was not accomplished due to weak signal strength

Hazardous near Earth asteroid mitigation campaign planning based on uncertain information on fundamental asteroid characteristics

Given a limited warning time, an asteroid impact mitigation campaign would hinge on uncertainty-based information consisting of remote observational data of the identified Earth-threatening object, general knowledge of near-Earth asteroids (NEAs), and engineering judgment. Due to these ambiguities, the campaign credibility could be profoundly compromised. It is therefore imperative to comprehensively evaluate the inherent uncertainty in deflection and plan the campaign accordingly to ensure successful mitigation. This research demonstrates dual-deflection mitigation campaigns consisting of primary (instantaneous/quasi-instantaneous) and secondary (slow-push) deflection missions, where both deflection efficiency and campaign credibility are taken into account. The results of the dual-deflection campaign analysis show that there are trade-offs between the competing aspects: the launch cost, mission duration, deflection distance, and the confidence in successful deflection. The design approach is found to be useful for multi-deflection campaign planning, allowing us to select the best possible combination of missions from a catalogue of campaign options, without compromising the campaign credibility

Development of Scale and Rotation Invariant Neural Network based Technique for Detection of Dielectric Contrast Concealed Targets with Millimeter Wave System

The detection of concealed targets beneath a person’s clothing from standoff distance is an important task for protection and the security of a person in a crowded place like shopping malls, airports and playground stadium, etc. The detection capability of the concealed weapon depends on a lot of factors likes, a collection of back scattered data, dielectric property and a thickness of covering cloths, the hidden object, standoff distance and the probability of false alarm owing to objectionable substances. Though active millimeter wave systems have used to detect weapons under cloths, but still more attention is required to detect the target likes a gun, knife, and matchbox. To observe such problems, active V-band (59 GHz- 61 GHz) MMW radar with the help of artificial neural network (ANN) has been demonstrated for non-metallic as well as metallic concealed target detection. To validate ANN, the signature of predefined targets is matched with the signature of validated data with the help of the correlation coefficient. The proposed technique has good capability to distinguish concealed targets under various cloths.

A Physical optics simulator for multireflector THz imaging systems

This article presents a physical optics-based simulator for the analysis of terahertz (THz) imaging systems. The simulation starts by calculating the electromagnetic interactions inside the multireflector system and the incident field that the focusing system creates on the target under inspection. In a second step, the electric field that the modeled target scatters back to the system receiver, is also calculated. This allows to predict the imaging behavior of the system for different targets before manufacturing. The simulator results are validated by using measurements from an existing 300-GHz standoff imaging system. This contribution aims to help in the development of better imaging systems for security applications in the near future.Atlantic Research Center for Information and Communication TechnologiesMinisterio de Economía y Competitividad | Ref. TEC2015-65353-RMinisterio de Economía y Competitividad | Ref. TEC2015-73908-JINAgencia Estatal de Investigación | Ref. TEC2017-87061-C3-1-RXunta de Galicia | Ref. GRC2015/01

Application Of Antenna Synthesis And Digital Signal Processing Techniques For Active Millimeter-wave Imaging Systems

Millimeter-wave imaging has gathered attention in recent years for its ability to penetrate clothing, thin layers of soils, and certain construction materials. However, image quality remains a challenge that needs to be addressed. One way of improving image quality is by increasing the dimensions of the collecting aperture. A sparse array can be used to synthesize a larger aperture with a limited set of relatively small detectors. In this research we design, build, and test a test-bed having an active source at 94 GHz and an array of coherent detectors, mounted on arms that extend radially on a rotary table. Using this test bed a circular area with a maximum diameter of 900 mm can be scanned. The signal is down-converted using heterodyne receivers with digital in-phase and quadrature detection. Signal correlation is performed using the digitized data, which is stored for post-processing, electronic focusing, and image reconstruction. Near-field imaging using interferometric reconstructions is achieved using electronic focusing. Imaging tests show the ability of the system to generate imagery of concealed and unconcealed objects at distances between 400 and 700 mm. A study of the effects of redundant and nonredundant configurations on image quality for 4 common detector configurations is presented. In this document we show that an active sparse-aperture imaging system using digital correlators is a viable way to generate millimeter-wave images

Determining bridge deck deterioration through the use of 3D photogrammetry

The bridge inspection industry has yet to utilize a rapidly growing technology that shows promise to help improve the inspection process. This thesis investigates the abilities that 3D photogrammetry is capable of providing to the bridge inspector for a number of deterioration mechanisms. The technology can provide information about the surface condition of some bridge components, primarily focusing on the surface defects of a concrete bridge which include cracking, spalling and scaling. Testing was completed using a Canon EOS 7D camera which then processed photos using AgiSoft PhotoScan to align the photos and develop models. Further processing of the models was done using ArcMap in the ArcGIS 10 program to view the digital elevation models of the concrete surface. Several experiments were completed to determine the ability of the technique for the detection of the different defects. The cracks that were able to be resolved in this study were a 1/8 inch crack at a distance of two feet above the surface. 3D photogrammetry was able to be detect a depression of 1 inch wide with 3/16 inch depth which would be sufficient to measure any scaling or spalling that would be required be the inspector. The percentage scaled or spalled was also able to be calculated from the digital elevation models in ArcMap. Different camera factors including the distance from the defects, number of photos and angle, were also investigated to see how each factor affected the capabilities. 3D photogrammetry showed great promise in the detection of scaling or spalling of the concrete bridge surface