Development and Testing of a Fractal Analysis Algorithm for Face Recognition

Following an earlier development for fingerprints by Deal (1) and Stoffa (2), it was suggested that this algorithm may work on faces (or more precisely, face images). First, this work transformed a 2-D electronic image file of a human face into a numeric system via a similar random walk process by Deal and Stoffa. Second, the numeric system was analyzed, and the numeric system may then be tested against a database of similarly converted images. The testing determined whether the subject of the image is part of the database. Finally, the efficiency, quickness, and accuracy of such an algorithm were tested and conclusions about the general effectiveness were made.;The algorithm employed a Random Walk analysis of digital photographs of human faces for a fixed number of binary images which were generated from the source photograph using a Boolean conversion scheme. The Random Walk generated a series of transition probabilities for a particular scale. In short, the numeric system used to describe the face will consisted of two dimensions of data---scale and binary image. The numeric system for a particular source photograph was tested against a database of similarly constructed systems to determine whether the subject of the source photograph was in the database.;For the purpose of this work, a database of 400 images was constructed from 167 individual subjects using the FERET database. The 400 images where then analyzed, and tested against the database to determine whether the algorithm could find the subjects in the database. The algorithm was able, in its best configuration, to identify correctly the subjects of 168 of the 400 photographs. However, the total time to run an image (after capture by a digital camera) to database comparison was only 62 seconds, which represents a substantial improvement over previous systems

Convex Graph Invariant Relaxations For Graph Edit Distance

The edit distance between two graphs is a widely used measure of similarity that evaluates the smallest number of vertex and edge deletions/insertions required to transform one graph to another. It is NP-hard to compute in general, and a large number of heuristics have been proposed for approximating this quantity. With few exceptions, these methods generally provide upper bounds on the edit distance between two graphs. In this paper, we propose a new family of computationally tractable convex relaxations for obtaining lower bounds on graph edit distance. These relaxations can be tailored to the structural properties of the particular graphs via convex graph invariants. Specific examples that we highlight in this paper include constraints on the graph spectrum as well as (tractable approximations of) the stability number and the maximum-cut values of graphs. We prove under suitable conditions that our relaxations are tight (i.e., exactly compute the graph edit distance) when one of the graphs consists of few eigenvalues. We also validate the utility of our framework on synthetic problems as well as real applications involving molecular structure comparison problems in chemistry.Comment: 27 pages, 7 figure

Design of a Multi-biometric Platform, based on physical traits and physiological measures: Face, Iris, Ear, ECG and EEG

Security and safety is one the main concerns both for governments and for private companies in the last years so raising growing interests and investments in the area of biometric recognition and video surveillance, especially after the sad happenings of September 2001. Outlays assessments of the U.S. government for the years 2001-2005 estimate that the homeland security spending climbed from $56.0 billions of dollars in 2001 to almost$100 billion of 2005. In this lapse of time, new pattern recognition techniques have been developed and, even more important, new biometric traits have been investigated and refined; besides the well-known physical and behavioral characteristics, also physiological measures have been studied, so providing more features to enhance discrimination capabilities of individuals. This dissertation proposes the design of a multimodal biometric platform, FAIRY, based on the following biometric traits: ear, face, iris EEG and ECG signals. In the thesis the modular architecture of the platform has been presented, together with the results obtained for the solution to the recognition problems related to the different biometrics and their possible fusion. Finally, an analysis of the pattern recognition issues concerning the area of videosurveillance has been discussed

Optimisation of detectors for the golden channel at a neutrino factory

That neutrinos have mass and mix is now well established experimentally. Measurements of the properties of neutrinos from both natural and man-made sources have measured the large mixing angles and mass squared differences. In order to fully understand the nature of the neutrino, and ultimately the lepton sector, a number of measurements remain to be made. The Neutrino Factory would produce an intense beam of muon neutrino (muon antineutrino) and electron antineutrino (electron neutrino) from the decay of muons creating an intense flux of neutrinos. Such a facility would be capable of constraining the already measured mixing parameters to unprecedented accuracy while achieving sensitivity to the measurement of the third mixing angle and leptonic CP violating phase unrivaled by other facilities. The golden channel is characterised by the observation of a primary muon of the opposite charge to that decaying at the source, however, since this signal is subdominant the large data sample of correct sign muons have the potential to produce backgrounds to the desired signal channel and as such understanding the cross-sections to high accuracy enables a far better understanding of the response of the detector. Making these measurements requires the optimisation of all aspects of the detectors used for the measurement of the interaction properties as well as those which search for the appearance of neutrino flavours not present at the source. Pixellated silicon detectors are capable of high resolution three dimensional track reconstruction and vertexing. In studying active pixel sensors (APS) it was sought to understand the feasibility of commercially available technology to perform vertexing at a detector positioned within 1~km of the neutrino factory source. Using such technology at this near detector would improve significantly the ability of the experiment to constrain the cross-sections of neutrinos. These measurements would be particularly important in understanding neutrino induced charm production since the decays, in particular of charged D mesons, can produce penetrating muons with the potential to confuse the extraction of the appearance of muon neutrino (muon antineutrino). The capability to observe the impact parameter of the decaying meson significantly improves the accuracy of any measurement of the charm production cross-section. A Magnetised Iron Neutrino Detector (MIND) of large mass (50-100 ktonne) has been studied as the far detector where high suppression of the beam inherent backgrounds can be achieved due to the powerful suppression of hadronic particles in iron. Particular focus has been given to the introduction of a realistic reconstruction of the signal and analysis which optimises the signal efficiency below 5 GeV which has been identified by theoretical studies as key to the accurate measurement of the oscillation parameters down to low values. Studies of this detector have led to the extraction of the expected response of the detector to both golden channel signals and demonstration of the power of such an analysis to the measurement of the remaining oscillation parameters. Using minimal assumptions in the digitization of the simulated signal, the reconstruction and analysis of a large data-set of neutrino interactions, including deep-inelastic scattering (DIS), quasi-elastic scattering (QEL) and resonant pion production (RES), in MIND has led to the extraction of response matrices predicting signal efficiency for both muon neutrino and muon antineutrino appearance with thresholds between 2-3 GeV while suppressing key beam inherent backgrounds to at or below the 10^-4 level. Such a response has been shown to open the possiblity of sensitivity to the measurement of leptonic CP violation through the measurement of the mixing complex phase delta down to theta13 of order 0.2 degrees for maximal violation and to most possible values from theta13 of order 1 degrees. Sensitivity to the measurement of theta13 and to the determination of the true mass hierarchy is maintained down to theta13 of order 0.25 degrees

A measurement of the electron neutrino component of the T2K beam using the near detector

T2K is a long baseline neutrino oscillation experiment located in Japan, with a 295km baseline and peak neutrino energy of 0:6 GeV. It is the first off-axis neutrino experiment where the beam is directed approximately 2.5° away from the detectors in order to produce a narrow-band neutrino beam. The experiment was designed to measure the mixing angle θ13 by measuring the neutrino oscillation process vμ -> ve. This measurement relies on the detection of electrons at the far detector from oscillations, and so it is vital to understand the size of the intrinsic ve component of the beam. A measurement of the intrinsic ve component of the T2K beam was performed using the ND280. An analysis that used all of the data taken by the ND280 from February 2010 until March 2011, a total of 1.09 x 10^20 POT, measured 67.7 +- 12.9(stat) +- 5.2(syst) CC ve interactions. The number of events corresponds to a ratio between data and simulation of 0.983+-0.191(stat)+-0.076(syst) and provides strong evidence that the neutrino flux is well simulated. The simulation from the intrinsic ve measurement was then combined with an analysis of vμ interactions in the ND280 to constrain the neutrino flux uncertainties. An idealised study that considered only statistical and flux systematic uncertainties concluded that the intrinsic ve analysis improved the constraint on the flux uncertainties compared to considering only the ND280 vμ analyses, with the effect most prominent at neutrino energies greater than 1 GeV

Robust signatures for 3D face registration and recognition

PhDBiometric authentication through face recognition has been an active area of research for the last few decades, motivated by its application-driven demand. The popularity of face recognition, compared to other biometric methods, is largely due to its minimum requirement of subject co-operation, relative ease of data capture and similarity to the natural way humans distinguish each other. 3D face recognition has recently received particular interest since three-dimensional face scans eliminate or reduce important limitations of 2D face images, such as illumination changes and pose variations. In fact, three-dimensional face scans are usually captured by scanners through the use of a constant structured-light source, making them invariant to environmental changes in illumination. Moreover, a single 3D scan also captures the entire face structure and allows for accurate pose normalisation. However, one of the biggest challenges that still remain in three-dimensional face scans is the sensitivity to large local deformations due to, for example, facial expressions. Due to the nature of the data, deformations bring about large changes in the 3D geometry of the scan. In addition to this, 3D scans are also characterised by noise and artefacts such as spikes and holes, which are uncommon with 2D images and requires a pre-processing stage that is speci c to the scanner used to capture the data. The aim of this thesis is to devise a face signature that is compact in size and overcomes the above mentioned limitations. We investigate the use of facial regions and landmarks towards a robust and compact face signature, and we study, implement and validate a region-based and a landmark-based face signature. Combinations of regions and landmarks are evaluated for their robustness to pose and expressions, while the matching scheme is evaluated for its robustness to noise and data artefacts

A Proposal for a Three Detector Short-Baseline Neutrino Oscillation Program in the Fermilab Booster Neutrino Beam

A Short-Baseline Neutrino (SBN) physics program of three LAr-TPC detectors located along the Booster Neutrino Beam (BNB) at Fermilab is presented. This new SBN Program will deliver a rich and compelling physics opportunity, including the ability to resolve a class of experimental anomalies in neutrino physics and to perform the most sensitive search to date for sterile neutrinos at the eV mass-scale through both appearance and disappearance oscillation channels. Using data sets of 6.6e20 protons on target (P.O.T.) in the LAr1-ND and ICARUS T600 detectors plus 13.2e20 P.O.T. in the MicroBooNE detector, we estimate that a search for muon neutrino to electron neutrino appearance can be performed with ~5 sigma sensitivity for the LSND allowed (99% C.L.) parameter region. In this proposal for the SBN Program, we describe the physics analysis, the conceptual design of the LAr1-ND detector, the design and refurbishment of the T600 detector, the necessary infrastructure required to execute the program, and a possible reconfiguration of the BNB target and horn system to improve its performance for oscillation searches.Comment: 209 pages, 129 figure

Security of multimodal biometric systems against spoof attacks

A biometric system is essentially a pattern recognition system being used in ad-versarial environment. Since, biometric system like any conventional security system is exposed to malicious adversaries, who can manipulate data to make the system ineffective by compromising its integrity. Current theory and de- sign methods of biometric systems do not take into account the vulnerability to such adversary attacks. Therefore, evaluation of classical design methods is an open problem to investigate whether they lead to design secure systems. In order to make biometric systems secure it is necessary to understand and evalu-ate the threats and to thus develop effective countermeasures and robust system designs, both technical and procedural, if necessary. Accordingly, the extension of theory and design methods of biometric systems is mandatory to safeguard the security and reliability of biometric systems in adversarial environments. In this thesis, we provide some contributions towards this direction. Among all the potential attacks discussed in the literature, spoof attacks are one of the main threats against the security of biometric systems for identity recognition. Multimodal biometric systems are commonly believed to be in-trinsically more robust to spoof attacks than systems based on a single biomet-ric trait, as they combine information coming from different biometric traits. However, recent works have question such belief and shown that multimodal systems can be misled by an attacker (impostor) even by spoofing only one of the biometric traits. Therefore, we first provide a detailed review of state-of-the-art works in multimodal biometric systems against spoof attacks. The scope ofstate-of-the-art results is very limited, since they were obtained under a very restrictive “worst-case” hypothesis, where the attacker is assumed to be able to fabricate a perfect replica of a biometric trait whose matching score distribu-tion is identical to the one of genuine traits. Thus, we argue and investigate the validity of “worst-case” hypothesis using large set of real spoof attacks and provide empirical evidence that “worst-case” scenario can not be representa- ixtive of real spoof attacks: its suitability may depend on the specific biometric trait, the matching algorithm, and the techniques used to counterfeit the spoofed traits. Then, we propose a security evaluation methodology of biometric systems against spoof attacks that can be used in real applications, as it does not require fabricating fake biometric traits, it allows the designer to take into account the different possible qualities of fake traits used by different attackers, and it exploits only information on genuine and impostor samples which is col- lected for the training of a biometric system. Our methodology evaluates the performances under a simulated spoof attack using model of the fake score distribution that takes into account explicitly different degrees of the quality of fake biometric traits. In particular, we propose two models of the match score distribution of fake traits that take into account all different factors which can affect the match score distribution of fake traits like the particular spoofed biometric, the sensor, the algorithm for matching score computation, the technique used to construct fake biometrics, and the skills of the attacker. All these factors are summarized in a single parameter, that we call “attack strength”. Further, we propose extension of our security evaluation method to rank several biometric score fusion rules according to their relative robustness against spoof attacks. This method allows the designer to choose the most robust rule according to the method prediction. We then present empirical analysis, using data sets of face and fingerprints including real spoofed traits, to show that our proposed models provide a good approximation of fake traits’ score distribution and our method thus providing an adequate estimation of the security1 of biometric systems against spoof attacks. We also use our method to show how to evaluate the security of different multimodal systems on publicly available benchmark data sets without spoof attacks. Our experimental results show that robustness of multimodal biometric systems to spoof attacks strongly depends on the particular matching algorithm, the score fusion rule, and the attack strength of fake traits. We eventually present evidence, considering a multimodal system based on face and fingerprint biometrics, that the proposed methodology to rank score fusion rules is capable of providing correct ranking of score fusion rules under spoof attacks