Homogeneous and Heterogeneous Face Recognition: Enhancing, Encoding and Matching for Practical Applications

Face Recognition is the automatic processing of face images with the purpose to recognize individuals. Recognition task becomes especially challenging in surveillance applications, where images are acquired from a long range in the presence of difficult environments. Short Wave Infrared (SWIR) is an emerging imaging modality that is able to produce clear long range images in difficult environments or during night time. Despite the benefits of the SWIR technology, matching SWIR images against a gallery of visible images presents a challenge, since the photometric properties of the images in the two spectral bands are highly distinct.;In this dissertation, we describe a cross spectral matching method that encodes magnitude and phase of multi-spectral face images filtered with a bank of Gabor filters. The magnitude of filtered images is encoded with Simplified Weber Local Descriptor (SWLD) and Local Binary Pattern (LBP) operators. The phase is encoded with Generalized Local Binary Pattern (GLBP) operator. Encoded multi-spectral images are mapped into a histogram representation and cross matched by applying symmetric Kullback-Leibler distance. Performance of the developed algorithm is demonstrated on TINDERS database that contains long range SWIR and color images acquired at a distance of 2, 50, and 106 meters.;Apart from long acquisition range, other variations and distortions such as pose variation, motion and out of focus blur, and uneven illumination may be observed in multispectral face images. Recognition performance of the face recognition matcher can be greatly affected by these distortions. It is important, therefore, to ensure that matching is performed on high quality images. Poor quality images have to be either enhanced or discarded. This dissertation addresses the problem of selecting good quality samples.;The last chapters of the dissertation suggest a number of modifications applied to the cross spectral matching algorithm for matching low resolution color images in near-real time. We show that the method that encodes the magnitude of Gabor filtered images with the SWLD operator guarantees high recognition rates. The modified method (Gabor-SWLD) is adopted in a camera network set up where cameras acquire several views of the same individual. The designed algorithm and software are fully automated and optimized to perform recognition in near-real time. We evaluate the recognition performance and the processing time of the method on a small dataset collected at WVU

Recognition capacity of biometric-based systems

Performance of biometrics-based recognition systems depends on various factors: database quality, image preprocessing, encoding techniques, etc. Given a biometric database and a selected encoding method, the capability of a recognition system is limited by the relationship between the number of classes that the recognition system can encode and the length of encoded data describing the template at a specific level of distortion. In this work, we evaluate constrained recognition capacity of biometric systems under the constraint of two global encoding techniques: Principal Component Analysis and Independent Component Analysis. The developed methodology is applied to predict capacity of different recognition channels formed during acquisition of different iris and face databases. The proposed approach relies on data modeling and involves classical detection and information theories. The major contribution is in providing a guideline on how to evaluate capabilities of large-scale biometric recognition systems in practice. Recognition capacity can also be promoted as a global quality measure of biometric databases

Entanglement criteria for microscopic-macroscopic systems

We discuss the conclusions that can be drawn on a recent experimental micro-macro entanglement test [F. De Martini, F. Sciarrino, and C. Vitelli, Phys. Rev. Lett. 100, 253601 (2008). The system under investigation is generated through optical parametric amplification of one photon belonging to an entangled pair. The adopted entanglement criterion makes it possible to infer the presence of entanglement before losses, that occur on the macrostate, under a specific assumption. In particular, an a priori knowledge of the system that generates the micro-macro pair is necessary to exclude a class of separable states that can reproduce the obtained experimental results. Finally, we discuss the feasibility of a micro-macro "genuine" entanglement test on the analyzed system by considering different strategies, which show that in principle a fraction epsilon, proportional to the number of photons that survive the lossy process, of the original entanglement persists in any losses regime.Comment: 11 pages, 10 figure

Gaussian Process Prior Variational Autoencoders

Variational autoencoders (VAE) are a powerful and widely-used class of models to learn complex data distributions in an unsupervised fashion. One important limitation of VAEs is the prior assumption that latent sample representations are independent and identically distributed. However, for many important datasets, such as time-series of images, this assumption is too strong: accounting for covariances between samples, such as those in time, can yield to a more appropriate model specification and improve performance in downstream tasks. In this work, we introduce a new model, the Gaussian Process (GP) Prior Variational Autoencoder (GPPVAE), to specifically address this issue. The GPPVAE aims to combine the power of VAEs with the ability to model correlations afforded by GP priors. To achieve efficient inference in this new class of models, we leverage structure in the covariance matrix, and introduce a new stochastic backpropagation strategy that allows for computing stochastic gradients in a distributed and low-memory fashion. We show that our method outperforms conditional VAEs (CVAEs) and an adaptation of standard VAEs in two image data applications.Comment: Accepted at 32nd Conference on Neural Information Processing Systems (NIPS 2018), Montr\'eal, Canad

Maximising the Utility of Enterprise Millimetre-Wave Networks

Millimetre-wave (mmWave) technology is a promising candidate for meeting the intensifying demand for ultra fast wireless connectivity, especially in high-end enterprise networks. Very narrow beam forming is mandatory to mitigate the severe attenuation specific to the extremely high frequency (EHF) bands exploited. Simultaneously, this greatly reduces interference, but generates problematic communication blockages. As a consequence, client association control and scheduling in scenarios with densely deployed mmWave access points become particularly challenging, while policies designed for traditional wireless networks remain inappropriate. In this paper we formulate and solve these tasks as utility maximisation problems under different traffic regimes, for the first time in the mmWave context. We specify a set of low-complexity algorithms that capture distinctive terminal deafness and user demand constraints, while providing near-optimal client associations and airtime allocations, despite the problems' inherent NP-completeness. To evaluate our solutions, we develop an NS-3 implementation of the IEEE 802.11ad protocol, which we construct upon preliminary 60GHz channel measurements. Simulation results demonstrate that our schemes provide up to 60% higher throughput as compared to the commonly used signal strength based association policy for mmWave networks, and outperform recently proposed load-balancing oriented solutions, as we accommodate the demand of 33% more clients in both static and mobile scenarios.Comment: 22 pages, 12 figures, accepted for publication in Computer Communication

An Accurate and Computationally Efficient Uniaxial Phenomenological Model for Steel and Fiber Reinforced Elastomeric Bearings

We present a uniaxial phenomenological model to accurately predict the complex hysteretic behavior of bolted steel reinforced elastomeric bearings and unbonded fiber reinforced elastomeric bearings. The proposed model is based on a set of only five parameters, directly associated with the graphical properties of the hysteresis loop, leads to the solution of an algebraic equation for the evaluation of the isolator restoring force, requires only one history variable, and can be easily implemented in a computer program. The proposed model is validated by means of experimental tests and numerical simulations. In particular, the results predicted analytically are compared with some experimental results selected from the literature. Furthermore, numerical accuracy and computational efficiency of the model are assessed by performing nonlinear time history analyses on a single degree of freedom mechanical system and comparing the results with those associated with a modified version of the celebrated Bouc-Wen model

A class of uniaxial phenomenological models for simulating hysteretic phenomena in rate-independent mechanical systems and materials

We present a general formulation of a class of uniaxial phenomenological models, able to accurately simulate hysteretic phenomena in rate-independent mechanical systems and materials, which requires only one history variable and leads to the solution of a scalar equation for the evaluation of the generalized force. Two specific instances of the class, denominated Bilinear and Exponential Models, are developed as an example to illustrate the peculiar features of the formulation. The Bilinear Model, that is one of the simplest hysteretic models which can be emanated from the proposed class, is first described to clarify the physical meaning of the quantities adopted in the formulation. Specifically, the potentiality of the proposed class is witnessed by the Exponential Model, able to simulate more complex hysteretic behaviors of rate-independent mechanical systems and materials exhibiting either kinematic hardening or softening. The accuracy and the computational efficiency of this last model are assessed by carrying out nonlinear time history analyses, for a single degree of freedom mechanical system having a rate-independent kinematic hardening behavior, subjected either to a harmonic or to a random force. The relevant results are compared with those obtained by exploiting the widely used Bouc–Wen Model

A Computational Strategy for Eurocode 8-Compliant Analyses of Reinforced Concrete Structures by Seismic Envelopes

A procedure is presented for performing Eurocode 8-compliant spectral analyses of reinforced concrete structures by means of seismic response envelopes. To account for global torsion effects in the computation of the supreme envelope an algorithmic rotational response spectrum is defined. The presented strategy turns out to be particularly appropriate for finite element models including accidental eccentricity due to mass shifting since seismic envelopes can be computed by making reference to a single structural model rather than to separate models characterized by different signs of the accidental eccentricity. The proposed procedure is theoretically formulated and numerically tested by analyzing a rotationally stiff and a rotationally flexible building as well as two irregular structures. Moreover, it is compared with an alternative formulation derived from a recently proposed strategy concerning accidental torsion. The results show that the proposed procedure is coherent with the analysis procedures provided by standard codes and computationally more efficient