Predictive models for multibiometric systems

Recognizing a subject given a set of biometrics is a fundamental pattern recognition problem. This paper builds novel statistical models for multibiometric systems using geometric and multinomial distributions. These models are generic as they are only based on the similarity scores produced by a recognition system. They predict the bounds on the range of indices within which a test subject is likely to be present in a sorted set of similarity scores. These bounds are then used in the multibiometric recognition system to predict a smaller subset of subjects from the database as probable candidates for a given test subject. Experimental results show that the proposed models enhance the recognition rate beyond the underlying matching algorithms for multiple face views, fingerprints, palm prints, irises and their combinations

Modeling and performance analysis of a UAV-based sensor network for improved ATR

Automatic Target Recognition (ATR) is computer processing of images or signals acquired by sensors with the purpose to identify objects of interest (targets). This technology is a critical element for surveillance missions. Over the past several years there has been an increasing trend towards fielding swarms of unattended aerial vehicles (UAVs) operating as sensor networks in the air. This trend offers opportunities of integration ATR systems with a UAV-based sensor network to improve the recognition performance. This dissertation addresses some of design issues of ATR systems, explores recognition capabilities of sensor networks in the presence of various distortions and analyzes the limiting recognition performance of sensor networks.;We assume that each UAV is equipped with an optical camera. A model based recognition method for single and multiple frames is introduced. A complete ATR system, including detection, segmentation, recognition and clutter rejection, is designed and tested using synthetic and realistic images. The effects of environmental conditions on target recognition are also investigated.;To analyze and predict ATR performance of a recognition sensor network, a general methodology from information theory view point is used. Given the encoding method, the recognition system is analyzed using a recognition channel. The concepts of recognition capacity, error exponents and probability of outage are defined and derived for a PCA-based ATR system. Both the case of a single encoded image and the case of encoded correlated multiple frames are analyzed. Numerical evaluations are performed. Finally we discuss the joint recognition and communication problems. Three scenarios of a two node recognition sensor network are analyzed. The communication and recognition performances for each scenario are evaluated numerically

Optical scattering for security applications

Laser Surface Authentication (LSA) has emerged in recent years as a potentially disruptive tracking and authentication technology. A strong need for such a solution in a variety of industries drove the implementation of the technology faster than the scientific understanding could keep up. The drive to miniaturise and simplify, the need to be robust against real-world problems like damage and misuse, and not least, intellectual curiosity, make it clear that a firmer scientific footing is important as the technology matures. Existing scattering and biometric work are reviewed, and LSA is introduced as a technology. The results of field-work highlight the restrictions which are encountered when the technology is applied. Analysis of the datasets collected in the trial provide, first, an indication of the performance of LSA under real-world conditions and, second, insight into the potential shortcomings of the technique. Using the particulars of the current sensor’s geometry, the LSA signal is characterised. Measurements are made of the decorrelation of the signature with linear and rotational offsets, and it is concluded that while surface microstructure has a strong impact on the rate of decorrelation, this dependency is not driven by the surface’s feature size. A new series of experiments examine that same decorrelation for interference effects with different illumination conditions, and conclude that laser speckle is not an adequate explanation for the phenomenon. The results of this experimental work inform a mathematical description of LSA based on a combination of existing bi-static scattering models used in physics and ray-tracing, which is implemented numerically. The results of the model are found to be a good fit to experimental work, and new predictions are made about LSA