Eurographics Digital Library
http://diglib.eg.org:80
The Eurographics DL digital repository system captures, stores, indexes, preserves, and distributes digital research material.2024-03-19T13:36:48ZEfficient and High Performing Biometrics: Towards Enabling Recognition in Embedded Domains
https://diglib.eg.org:443/handle/10.2312/3543935
Efficient and High Performing Biometrics: Towards Enabling Recognition in Embedded Domains
Boutros, Fadi
The growing need for reliable and accurate recognition solutions along with the recent innovations in deep learning methodologies has reshaped the research landscape of biometric recognition. Developing efficient biometric solutions is essential to minimize the required computational costs, especially when deployed on embedded and low-end devices. This drives the main contributions of this work, aiming at enabling wide application range of biometric technologies.
Towards enabling wider implementation of face recognition in use cases that are extremely limited by computational complexity constraints, this thesis presents a set of efficient models for accurate face verification, namely MixFaceNets. With a focus on automated network architecture design, this thesis is the first to utilize neural architecture search to successfully develop a family of lightweight face-specific architectures, namely PocketNets. Additionally, this thesis proposes a novel training paradigm based on knowledge distillation (KD), the multi-step KD, to enhance the verification performance of compact models. Towards enhancing face recognition accuracy, this thesis presents a novel margin-penalty softmax loss, ElasticFace, that relaxes the restriction of having a single fixed penalty margin.
Occluded faces by facial masks during the recent COVID-19 pandemic presents an emerging challenge for face recognition. This thesis presents a solution that mitigates the effects of wearing a mask and improves masked face recognition performance. This solution operates on top of existing face recognition models and thus avoids the high cost of retraining existing face recognition models or deploying a separate solution for masked face recognition.
Aiming at introducing biometric recognition to novel embedded domains, this thesis is the first to propose leveraging the existing hardware of head-mounted displays for identity verification of the users of virtual and augmented reality applications. This is additionally supported by proposing a compact ocular segmentation solution as a part of an iris and periocular recognition pipeline. Furthermore, an identity-preserving synthetic ocular image generation approach is designed to mitigate potential privacy concerns related to the accessibility to real biometric data and facilitate the further development of biometric recognition in new domains.
2022-06-14T00:00:00ZPhysically Based Modeling of Micro-Appearance
https://diglib.eg.org:443/handle/10.2312/3543934
Physically Based Modeling of Micro-Appearance
Huang, Weizhen
This dissertation addresses the challenges of creating photorealistic images by focusing on generating and rendering microscale details and irregularities, because a lack of such imperfections is usually the key aspect of telling a photograph from a synthetic, computer-generated image.
In Chapter 3, we model the fluid flow on soap bubbles, which demonstrate iridescent beauty due to their micrometer-scale thickness. Instead of approximating the variation in film thickness with random noise textures, this work incorporates the underlying mechanics that drive such fluid flow, namely the Navier-Stokes equations, which include factors such as surfactant concentration, Marangoni surface tension, and evaporation. We address challenges such as the singularity at poles in spherical coordinates and the need for extremely small step sizes in a stiff system to simulate a wide range of dynamic effects. As a result, our approach produces soap bubble renderings that match real-world footage.
Chapter 4 explores hair rendering. Existing models based on the Marschner model split the scattering function into a longitudinal and an azimuthal component. While this separation benefits importance sampling, it lacks a physical ground and does not match measurements. We propose a novel physically based hair scattering model, representing hair as cylinders with microfacet roughness. We reveal that the focused highlight in the forward-scattering direction observed in the measurement is a result of the rough cylindrical geometry itself. Additionally, our model naturally extends to elliptical hair fibers.
A much related topic, feather rendering, is discussed in Chapter 5. Unlike human hairs, feathers possess unique substructures, such as barbs and barbules with irregular cross-sections, the existing pipeline of modeling feathers using hair shaders therefore fails to accurately describe their appearance. We propose a model that directly accounts for these multi-scale geometries by representing feathers as collections of barb primitives and incorporating the contributions of barbule cross-sections using a normal distribution function. We demonstrate the effectiveness of our model on rock dove neck feathers, showing close alignment with measurements and photographs.
2023-07-11T00:00:00ZHierarchical Gradient Domain Vector Field Processing
https://diglib.eg.org:443/handle/10.2312/3543933
Hierarchical Gradient Domain Vector Field Processing
Lee, Sing Chun
Vector fields are a fundamental mathematical construct for describing flow-field-related problems in science and engineering. To solve these types of problems effectively on a discrete surface, various vector field representations are proposed using finite dimensional bases, a discrete connection, and an operator approach. Furthermore, for computational efficiency, quadratic Dirichlet energy is preferred to measure the smoothness of the vector field in the gradient domain. However, while quadratic energy gives a simple linear system, it does not support real-time vector field processing on a high-resolution mesh without extensive GPU parallelization. To this end, this dissertation describes an efficient hierarchical solver for vector field processing. Our method extends the successful multigrid design for interactive signal processing on meshes using an induced vector field prolongation combing it with novel speedup techniques. We formulate a general way for extending scalar field prolongation to vector fields. Focusing on triangle meshes, our convergence study finds that a standard multigrid does not achieve fast convergence due to the poorly-conditioned system matrix. We observe a similar performance in standard single-level iterative methods such as the Jacobi, Gauss-Seidel, and conjugate gradient methods. Therefore, we compare three speedup techniques -- successive over-relaxation, smoothed prolongation, and Krylov subspace update, and incorporate them into our solver. Finally, we demonstrate our solver on useful applications such as logarithmic map computation and discuss the applications to other hierarchies such as texture grids, followed by the conclusion and future work.
2023-07-31T00:00:00ZInverse Shape Design with Parametric Representations: Kirchhoff Rods and Parametric Surface Models
https://diglib.eg.org:443/handle/10.2312/3543932
Inverse Shape Design with Parametric Representations: Kirchhoff Rods and Parametric Surface Models
Hafner, Christian
Inverse design problems in fabrication-aware shape optimization are typically solved on discrete representations such as polygonal meshes. This thesis argues that there are benefits to treating these problems in the same domain as human designers, namely, the parametric one. One reason is that discretizing a parametric model usually removes the capability of making further manual changes to the design, because the human intent is captured by the shape parameters. Beyond this, knowledge about a design problem can sometimes reveal a structure that is present in a smooth representation, but is fundamentally altered by discretizing. In this case, working in the parametric domain may even simplify the optimization task. We present two lines of research that explore both of these aspects of fabrication-aware shape optimization on parametric representations.
The first project studies the design of plane elastic curves and Kirchhoff rods, which are common mathematical models for describing the deformation of thin elastic rods such as beams, ribbons, cables, and hair. Our main contribution is a characterization of all curved shapes that can be attained by bending and twisting elastic rods having a stiffness that is allowed to vary across the length. Elements like these can be manufactured using digital fabrication devices such as 3d printers and digital cutters, and have applications in free-form architecture and soft robotics.
We show that the family of curved shapes that can be produced this way admits geometric description that is concise and computationally convenient. In the case of plane curves, the geometric description is intuitive enough to allow a designer to determine whether a curved shape is physically achievable by visual inspection alone. We also present shape optimization algorithms that convert a user-defined curve in the plane or in three dimensions into the geometry of an elastic rod that will naturally deform to follow this curve when its endpoints are attached to a support structure. Implemented in an interactive software design tool, the rod geometry is generated in real time as the user edits a curve and enables fast prototyping.
The second project tackles the problem of general-purpose shape optimization on CAD models using a novel variant of the extended finite element method (XFEM). Our goal is the decoupling between the simulation mesh and the CAD model, so no geometry-dependent meshing or remeshing needs to be performed when the CAD parameters change during optimization. This is achieved by discretizing the embedding space of the CAD model, and using a new high-accuracy numerical integration method to enable XFEM on free-form elements bounded by the parametric surface patches of the model. Our simulation is differentiable from the CAD parameters to the simulation output, which enables us to use off-the-shelf gradient-based optimization procedures. The result is a method that fits seamlessly into the CAD workflow because it works on the same representation as the designer, enabling the alternation of manual editing and fabrication-aware optimization at will.
2023-05-01T00:00:00Z