Selection of Wavelet Subbands Using Genetic Algorithm for Face Recognition

Abstract. In this paper, a novel representation called the subband face is proposed for face recognition. The subband face is generated from selected subbands obtained using wavelet decomposition of the original face image. It is surmised that certain subbands contain information that is more significant for discriminating faces than other subbands. The problem of subband selection is cast as a combinatorial optimization problem and genetic algorithm (GA) is used to find the optimum subband combination by maximizing Fisher ratio of the training features. The performance of the GA selected subband face is evaluated using three face databases and compared with other wavelet-based representations.

Current Options for Visualization of Local Deformation in Modern Shape Analysis Applied to Paleobiological Case Studies

In modern shape analysis, deformation is quantified in different ways depending on the algorithms used and on the scale at which it is evaluated. While global affine and non-affine deformation components can be decoupled and computed using a variety of methods, the very local deformation can be considered, infinitesimally, as an affine deformation. The deformation gradient tensor F can be computed locally using a direct calculation by exploiting triangulation or tetrahedralization structures or by locally evaluating the first derivative of an appropriate interpolation function mapping the global deformation from the undeformed to the deformed state. A suitable function is represented by the thin plate spline (TPS) that separates affine from non-affine deformation components. F, also known as Jacobian matrix, encodes both the locally affine deformation and local rotation. This implies that it should be used for visualizing primary strain directions (PSDs) and deformation ellipses and ellipsoids on the target configuration. Using C = FTF allows, instead, one to compute PSD and to visualize them on the source configuration. Moreover, C allows the computation of the strain energy that can be evaluated and mapped locally at any point of a body using an interpolation function. In addition, it is possible, by exploiting the second-order Jacobian, to calculate the amount of the non-affine deformation in the neighborhood of the evaluation point by computing the body bending energy density encoded in the deformation. In this contribution, we present (i) the main computational methods for evaluating local deformation metrics, (ii) a number of different strategies to visualize them on both undeformed and deformed configurations, and (iii) the potential pitfalls in ignoring the actual three-dimensional nature of F when it is evaluated along a surface identified by a triangulation in three dimensions

Robust Face Recognition for Data Mining

While the technology for mining text documents in large databases could be said to be relatively mature, the same cannot be said for mining other important data types such as speech, music, images and video. Yet these forms of multimedia data are becoming increasingly prevalent on the internet and intranets as bandwidth rapidly increases due to continuing advances in computing hardware and consumer demand. An emerging major problem is the lack of accurate and efficient tools to query these multimedia data directly, so we are usually forced to rely on available metadata such as manual labeling. Currently the most effective way to label data to allow for searching of multimedia archives is for humans to physically review the material. This is already uneconomic or, in an increasing number of application areas, quite impossible because these data are being collected much faster than any group of humans could meaningfully label them - and the pace is accelerating, forming a veritable explosion of non-text data. Some driver applications are emerging from heightened security demands in the 21st century, postproduction of digital interactive television, and the recent deployment of a planetary sensor network overlaid on the internet backbone

Statistical modelling of local features of three-dimensional shapes

The rapid development of 3D imaging technology allows data to be collected directly in three-dimensional space. The high accuracy of the images requires further investigations on digitised objects, especially of local features. In the last decade, 3D Local features have played an important role in recognising and modelling real-world 3D objects. This thesis introduces a series of methods for 3D local features, including automatic keypoints detection, 3D model construction with curves, local region detection and statistical analysis of local features. Those methods are not only to build 3D local feature descriptors but also have a wide range of applications, such as shape comparison in medical facial treatments and evolutionary researches in biology. Conventional shape analysis, limited by the data-collection technology, project 3D objects into 2D space to analyse or focus on 3D discrete points which are not close to each other. Those points of anatomical meanings are called landmarks. Researchers used to manually place the landmarks on 2D or 3D images by eyes, but it generates the operator error which is not of interest but has a large influence on shape analysis. This thesis introduces a novel method to automatically estimate the landmarks on 3D models using Bayesian statistics. The Procrustes matching of the landmark sets shows that the variation of Bayesian placements is much smaller than the manual placements. Local shapes like $``$ridges$"$ and $``$valleys$"$, which are considered to contain rich geometric information, can be estimated based on landmarks. Existing methods rely heavily on landmarks, but in most cases, the number of landmarks is not enough and adding extra ones are time-and-labour consuming. A flexible and user-customisable method is introduced in this thesis to deal with complex surfaces marked with as few landmarks as possible. A simulation study is conducted, and the result shows that the method is stable and efficient in terms of local feature description. After the 3D curve is estimated, methods to analyse the local features using the curves are discussed. An algorithm to flexibly dissect the surface along the estimated curve is developed for extracting local pieces or divide the surface into pieces. The novelty of this method is that it applies directly on 3D shapes and dissects the shape along any 3D curves, such as the lip edge on a human facial model. Besides the novel method for 3D shapes, curvatures, which reflect the bending amount along the curves, are calculated. The curvatures of the same local feature on different individuals are aligned to analyse the average shape difference of groups, such as gender and age. A reconstruction procedure from the curvatures is discussed and the effect of noise on choosing the degree of freedom in smoothing is investigated. Another application of the estimated curves is in benchmarking the performance of different 3D camera systems. A new camera system developed by NCTech\textsuperscript{\textregistered}, Edinburgh, is assessed using the evaluation outcome of facial deformity surgeries in Brazil. It is designed to be child-friendly, portable and low-cost. Validation studies are carried out at three stages of the development, and both landmarks and curves are used to evaluate the performance of the new camera system on estimating local features in comparison with mature products from DI4D\textsuperscript{\textregistered} and Artec\textsuperscript{\textregistered}

Face Recognition with One Sample Image per Class

There are two main approaches for face recognition with variations in lighting conditions. One is to represent images with features that are insensitive to illumination in the first place. The other main approach is to construct a linear subspace for every class under the different lighting conditions. Both of these techniques are successfully applied to some extent in face recognition, but it is hard to extend them for recognition with variant facial expressions. It is observed that features insensitive to illumination are highly sensitive to expression variations, which result in face recognition with changes in both lighting conditions and expressions a difficult task. We propose a new method called Affine Principal Components Analysis in an attempt to solve both of these problems. This method extract features to construct a subspace for face representation and warps this space to achieve better class separation. The proposed technique is evaluated using face databases with both variable lighting and facial expressions. We achieve more than 90% accuracy for face recognition by using only one sample image per class

Development of an Atlas-Based Segmentation of Cranial Nerves Using Shape-Aware Discrete Deformable Models for Neurosurgical Planning and Simulation

Twelve pairs of cranial nerves arise from the brain or brainstem and control our sensory functions such as vision, hearing, smell and taste as well as several motor functions to the head and neck including facial expressions and eye movement. Often, these cranial nerves are difficult to detect in MRI data, and thus represent problems in neurosurgery planning and simulation, due to their thin anatomical structure, in the face of low imaging resolution as well as image artifacts. As a result, they may be at risk in neurosurgical procedures around the skull base, which might have dire consequences such as the loss of eyesight or hearing and facial paralysis. Consequently, it is of great importance to clearly delineate cranial nerves in medical images for avoidance in the planning of neurosurgical procedures and for targeting in the treatment of cranial nerve disorders. In this research, we propose to develop a digital atlas methodology that will be used to segment the cranial nerves from patient image data. The atlas will be created from high-resolution MRI data based on a discrete deformable contour model called 1-Simplex mesh. Each of the cranial nerves will be modeled using its centerline and radius information where the centerline is estimated in a semi-automatic approach by finding a shortest path between two user-defined end points. The cranial nerve atlas is then made more robust by integrating a Statistical Shape Model so that the atlas can identify and segment nerves from images characterized by artifacts or low resolution. To the best of our knowledge, no such digital atlas methodology exists for segmenting nerves cranial nerves from MRI data. Therefore, our proposed system has important benefits to the neurosurgical community

Investigation of the Nonlinear Optical Properties of Metamaterials by Second Harmonic Generation

Metamaterials have attracted tremendous attention in the past decade because they allow researchers to engineer new optical properties by designing a new optical material. In particular, metamaterials are regarded as the key technology paving the way to an optical revolution, from medical applications to all-optical networks. Metamaterials are sub-wavelength metallic nanostructures which owe their optical properties to the formation of a so-called plasmon, or collective electron oscillation. Each nanostructure can be regarded as a meta-atom forming an homogeneous optical material whose optical resonance features depend on three main parameters: The shape of the nanostructure, and the dielectric functions of both the material used and the surrounding environment. Even though the nonlinear optical features are bound to play a central role for future applications, the underlying light conversion process has been unknown up to now. Moreover, hardly any of the plasmonic features found yet its way to an actual application. In the present work, one makes use of the symmetry sensitivity and spectral information carried in the nonlinear process of second harmonic generation (SHG). Two types of investigations are carried out: First, gold nanostructures sharing symmetry feature variations are investigated to characterize the SHG in metamaterials. Second, simple gold nanowires are used to pattern the surface of an SHG-active host to investigate their application as an optical catalyst enhancing the SHG yield of the underlying crystal. For the first time, broad SHG spectra are recorded from a variety of nanostructures sharing geometrical features. SHG is measured even from nanostructures described as centrosymmetric, or from tensor components expected to be symmetry forbidden. This work provides valuable insights into the potential role played by nanoscopic surface defects and irregularities resulting from the top-down electron-beam lithography (EBL) fabrication process. The amplification model developed for the concept of an optical catalyst proves not to be sophisticated enough to fully explain the recorded SHG results. However, nanostructures fabricated on the surface of RMnO3 and Cr2O3 crystals display tweaked SHG features with respect to the known features from these nonlinear model systems. This is a clear indication of convolutions of the plasmonic process with a sample-specific response. Although the exact process could not be pinpointed, a certain dependence of the SHG yield on the environment of the nanowires has been highlighted and requires to take a closer look in future investigations

Multi-Object Tracking System based on LiDAR and RADAR for Intelligent Vehicles applications

El presente Trabajo Fin de Grado tiene como objetivo el desarrollo de un Sistema de Detección y Multi-Object Tracking 3D basado en la fusión sensorial de LiDAR y RADAR para aplicaciones de conducción autónoma basándose en algoritmos tradicionales de Machine Learning. La implementación realizada está basada en Python, ROS y cumple requerimientos de tiempo real. En la etapa de detección de objetos se utiliza el algoritmo de segmentación del plano RANSAC, para una posterior extracción de Bounding Boxes mediante DBSCAN. Una Late Sensor Fusion mediante Intersection over Union 3D y un sistema de tracking BEV-SORT completan la arquitectura propuesta.This Final Degree Project aims to develop a 3D Multi-Object Tracking and Detection System based on the Sensor Fusion of LiDAR and RADAR for autonomous driving applications based on traditional Machine Learning algorithms. The implementation is based on Python, ROS and complies with real-time requirements. In the Object Detection stage, the RANSAC plane segmentation algorithm is used, for a subsequent extraction of Bounding Boxes using DBSCAN. A Late Sensor Fusion using Intersection over Union 3D and a BEV-SORT tracking system complete the proposed architecture.Grado en Ingeniería en Electrónica y Automática Industria