Generic Elastic Model Research Articles

Parametric Modeling of a Long-Range Aircraft under Consideration of Engine-Wing Integration

The purpose of this paper is to investigate the influence of the engine position and mass as well as the pylon stiffness on the aeroelastic stability of a long-range wide-body transport aircraft. As reference configuration, DLR’s (German Aerospace Center/Deutsches Zentrum für Luft und Raumfahrt) generic aircraft configuration DLR-D250 is taken. The structural, mass, loads, and optimization models for the reference and a modified configuration with different engine and pylon parameters are set up using DLR’s automatized aeroelastic design process cpacs-MONA. At first, the cpacs-MONA process with its capabilities for parametric modeling of the complete aircraft and in particular the set-up of a generic elastic pylon model is unfolded. Then, the influence of the modified engine-wing parameters on the flight loads of the main wing is examined. The resulting loads are afterward used to structurally optimize the two configurations component wise. Finally, the results of post-cpacs-MONA flutter analyses performed for the two optimized aircraft configurations with the different engine and pylon characteristics are discussed. It is shown that the higher mass and the changed position of the engine slightly increased the flutter speed. Although the lowest flutter speeds for both configurations occur at a flutter phenomenon of the horizontal tail-plane outside of the aeroelastic stability envelope.

Local Comparative Decimal Pattern for Face Recognition

Rapid growth of social networks has provided an extraordinary medium to share a large volume of photographs online. This calls for designing efficient face recognition techniques that are applicable to images with low resolutions and arbitrary poses. This paper proposes a new pose invariant face recognition method for low resolution images using only a single training sample. A 3D model, reconstructed using Generic Elastic Model (3D GEM) from a frontal view training sample, is used to generate a set of nonfrontal gallery face images. The face region of the nonfrontal query sample is then extracted using the same landmark detection technique as in the 3D GEM algorithm. Afterwards, a novel texture representation technique called Local Comparative Decimal Pattern (LCDP) is proposed to extract features from each of the training and query samples. A set of experimental results on the ORL, Georgia Tech (GT), and LFW face databases demonstrates the efficiency of the proposed method compared to other state-of-the-art approaches.

Computer-aided superimposition via reconstructing and matching 3D faces to 3D skulls for forensic craniofacial identifications

Identification of human remains via craniofacial superimposition (CS) is one of the prominent research areas in the forensic sciences. CS makes use of imaging techniques to identify an unknown skull by matching it with the available face photographs of missing individuals. Life-size enlargement of the face image and orientating the skull to correspond to the posture seen in the face photograph are the two main problems that affect identification accuracy with both the conventional and the computer-aided methods. Unlike the existing techniques, this research proposes a 3D skull–3D face model superimposition (3D–3D) approach to address the above two issues. The proposed method commences by reconstructing the 3D face model from a given 2D face image using the mean simplified generic elastic model, followed by registering the face model to a 3D skull along the jaw line using the analytical curvature B-spline (AC B-spline). The accuracy index of the registration is then evaluated to suggest the degree to which the face image corresponds to a skull. The superimpositions of positive and negative cases were conducted on a set of 3D skulls versus a set of 2D face images. The accuracy indices of the registration results suggest that the AC B-spline is more robust in 3D–3D superimposition compared to the other existing methods. The full experimental results have demonstrated the potential of the proposed method as an assistive tool to the forensic scientists for craniofacial identifications.

Concept of Lattice Mismatch and Emergence of Surface States in Two-dimensional Hybrid Perovskite Quantum Wells

Surface states are ubiquitous to semiconductors and significantly impact the physical properties and, consequently, the performance of optoelectronic devices. Moreover, surface effects are strongly amplified in lower dimensional systems such as quantum wells and nanostructures. Layered halide perovskites (LHPs) are two-dimensional solution-processed natural quantum wells where optoelectronic properties can be tuned by varying the perovskite layer thickness n, i.e., the number of octahedra spanning the layer. They are efficient semiconductors with technologically relevant stability. Here, a generic elastic model and electronic structure modeling are applied to LHPs heterostructures with various layer thickness. We show that the relaxation of the interface strain is triggered by perovskite layers above a critical thickness. This leads to the release of the mechanical energy arising from the lattice mismatch, which nucleates the surface reorganization and may potentially induce the formation of previously observed lower energy edge states. These states, which are absent in three-dimensional perovskites are anticipated to play a crucial role in the design of LHPs for optoelectronic systems.

From one to many: Pose-Aware Metric Learning for single-sample face recognition

Pose and illumination variations are very challenging for face recognition with a single sample per person (SSPP). In this paper, we address this issue by a Pose-Aware Metric Learning (PAML) approach. Our primary idea is “from one to many”: Synthesizing many images of sufficient pose and illumination variability from the single training image, based on which metric learning approach is applied to reduce these “synthesized” variations at each quantified pose. For this purpose, given a single frontal training image, a multi-depth generic elastic model and an extended generic elastic model are developed to synthesize facial images of the target pose with varying 3D shape (depth) and illumination variations respectively. To reduce these “synthesized” variability, Pose-Aware Metric spaces are separately learnt by linear regression analysis at each quantized pose, and pose-invariant recognition is performed in the corresponding metric space. By preserving the detailed texture and reducing the shape variability, the PAML method achieves an 100% accuracy on the Multi-PIE database under the test setting across poses, which is significantly better than the traditional methods that use a large generic image ensemble to learn the cross-pose transformations. On the more challenging setting across both poses and illuminations, PAML outperforms the recent deep learning approaches by over 10% accuracy.

2D facial expression recognition via 3D reconstruction and feature fusion

In this paper, a novel feature extraction method is proposed for facial expression recognition by extracting the feature from facial depth and 3D mesh alongside texture. Accordingly, the 3D Facial Expression Generic Elastic Model (3D FE-GEM) method is used to reconstruct an expression-invariant 3D model from the human face. Then, the texture, depth and mesh are extracted from the reconstructed face model. Afterwards, the Local Binary Pattern (LBP), proposed 3D High-Low Local Binary Pattern (3DH-LLBP) and Local Normal Binary Patterns (LNBPs) are applied to texture, depth and mesh of the face, respectively, to extract the feature from 2D images. Finally, the final feature vectors are generated through feature fusion and are classified by the Support Vector Machine (SVM). Convincing results are acquired for facial expression recognition on the CK+, CK, JAFFE and Bosphorus image databases compared to several state-of-the-art methods.

Spartans: Single-Sample Periocular-Based Alignment-Robust Recognition Technique Applied to Non-Frontal Scenarios.

In this paper, we investigate a single-sample periocular-based alignment-robust face recognition technique that is pose-tolerant under unconstrained face matching scenarios. Our Spartans framework starts by utilizing one single sample per subject class, and generate new face images under a wide range of 3D rotations using the 3D generic elastic model which is both accurate and computationally economic. Then, we focus on the periocular region where the most stable and discriminant features on human faces are retained, and marginalize out the regions beyond the periocular region since they are more susceptible to expression variations and occlusions. A novel facial descriptor, high-dimensional Walsh local binary patterns, is uniformly sampled on facial images with robustness toward alignment. During the learning stage, subject-dependent advanced correlation filters are learned for pose-tolerant non-linear subspace modeling in kernel feature space followed by a coupled max-pooling mechanism which further improve the performance. Given any unconstrained unseen face image, the Spartans can produce a highly discriminative matching score, thus achieving high verification rate. We have evaluated our method on the challenging Labeled Faces in the Wild database and solidly outperformed the state-of-the-art algorithms under four evaluation protocols with a high accuracy of 89.69%, a top score among image-restricted and unsupervised protocols. The advancement of Spartans is also proven in the Face Recognition Grand Challenge and Multi-PIE databases. In addition, our learning method based on advanced correlation filters is much more effective, in terms of learning subject-dependent pose-tolerant subspaces, compared with many well-established subspace methods in both linear and non-linear cases.

Real‐world gender classification via local Gabor binary pattern and three‐dimensional face reconstruction by generic elastic model

In this study, a novel method is proposed for gender classification by adding facial depth features to texture features. Accordingly, the three-dimensional (3D) generic elastic model is used to reconstruct the 3D model from human face using only a single 2D frontal image. Then, the texture and depth are extracted from the reconstructed face model. Afterwards, the local Gabor binary pattern (LGBP) is applied to both facial texture and reconstructed depth to extract the feature vectors from both texture and reconstructed depth images. Finally, by combining 2D and 3D feature vectors, the final LGBP histogram bins are generated and classified by the support vector machine. Favourable outcomes are acquired for gender classification on the labelled faces in the wild and FERET databases based on the proposed method compared to several state-of-the-arts in gender classification.

Real-World and Rapid Face Recognition Toward Pose and Expression Variations via Feature Library Matrix

In this paper, a novel method for face recognition under pose and expression variations is proposed from only a single image in the gallery. A 3D probabilistic facial expression recognition generic elastic model is proposed to reconstruct a 3D model from real-world human face using only a single 2D frontal image with/without facial expressions. Then, a feature library matrix (FLM) is generated for each subject in the gallery from all face poses by rotating the 3D reconstructed models and extracting features in the rotated face pose. Therefore, each FLM is subsequently rendered for each subject in the gallery based on triplet angles of face poses. In addition, before matching the FLM, an initial estimate of triplet angles is obtained from the face pose in probe images using an automatic head pose estimation approach. Then, an array of the FLM is selected for each subject based on the estimated triplet angles. Finally, the selected arrays from FLMs are compared with extracted features from the probe image by iterative scoring classification using the support vector machine. Convincing results are acquired to handle pose and expression changes on the Bosphorus, Face Recognition Technology (FERET), Carnegie Mellon University-Pose, Illumination, and Expression (CMU-PIE), and Labeled Faces in the Wild (LFW) face databases compared with several state-of-the-art methods in pose-invariant face recognition. The proposed method not only demonstrates an excellent performance by obtaining high accuracy on all four databases but also outperforms other approaches realistically.

Unrestricted pose-invariant face recognition by sparse dictionary matrix

In this paper, a novel method is proposed for real-world pose-invariant face recognition from only a single image in a gallery. A 3D Facial Expression Generic Elastic Model (3D FE-GEM) is proposed to reconstruct a 3D model of each human face using only a single 2D frontal image. Then, for each person in the database, a Sparse Dictionary Matrix (SDM) is created from all face poses by rotating the 3D reconstructed models and extracting features in the rotated face. Each SDM is subsequently rendered based on triplet angles of face poses. Before matching to SDM, an initial estimate of triplet angles of face poses is obtained in the probe face image using an automatic head pose estimation approach. Then, an array of the SDM is selected based on the estimated triplet angles for each subject. Finally, the selected arrays from SDMs are compared with the probe image by sparse representation classification. Convincing results were acquired to handle pose changes on the FERET, CMU PIE, LFW and video face databases based on the proposed method compared to several state-of-the-art in pose-invariant face recognition.

Expression-invariant three-dimensional face reconstruction from a single image by facial expression generic elastic models

An efficient method for expression-invariant three-dimensional (3-D) face reconstruction from a frontal face image with a variety of facial expressions (FE) using the FE generic elastic model (GEM) is proposed. Three generic models are employed for FE modeling in the generic elastic model (GEM) framework, which are combined based on the similarity distance around the lips. Exclusively, FE-GEM demonstrated that it is more precisely able to estimate a 3-D model of a frontal face, attaining a more robust and better quality 3-D face reconstruction under a variety of FEs compared to the original GEM approach. It is tested on an available 3-D face database and its accuracy and robustness are demonstrated compared to the GEM approach under a variety of FEs. Also, the FE-GEM method is tested on available two-dimensional face databases and a new synthesized pose is generated from gallery images for handling pose variations in face recognition.

Sparse Feature Extraction for Pose-Tolerant Face Recognition.

Automatic face recognition performance has been steadily improving over years of research, however it remains significantly affected by a number of factors such as illumination, pose, expression, resolution and other factors that can impact matching scores. The focus of this paper is the pose problem which remains largely overlooked in most real-world applications. Specifically, we focus on one-to-one matching scenarios where a query face image of a random pose is matched against a set of gallery images. We propose a method that relies on two fundamental components: (a) A 3D modeling step to geometrically correct the viewpoint of the face. For this purpose, we extend a recent technique for efficient synthesis of 3D face models called 3D Generic Elastic Model. (b) A sparse feature extraction step using subspace modeling and ℓ1-minimization to induce pose-tolerance in coefficient space. This in return enables the synthesis of an equivalent frontal-looking face, which can be used towards recognition. We show significant performance improvements in verification rates compared to commercial matchers, and also demonstrate the resilience of the proposed method with respect to degrading input quality. We find that the proposed technique is able to match non-frontal images to other non-frontal images of varying angles.

Gender and Ethnicity Specific Generic Elastic Models from a Single 2D Image for Novel 2D Pose Face Synthesis and Recognition

In this paper, we propose a novel method for generating a realistic 3D human face from a single 2D face image for the purpose of synthesizing new 2D face images at arbitrary poses using gender and ethnicity specific models. We employ the Generic Elastic Model (GEM) approach, which elastically deforms a generic 3D depth-map based on the sparse observations of an input face image in order to estimate the depth of the face image. Particularly, we show that Gender and Ethnicity specific GEMs (GE-GEMs) can approximate the 3D shape of the input face image more accurately, achieving a better generalization of 3D face modeling and reconstruction compared to the original GEM approach. We qualitatively validate our method using publicly available databases by showing each reconstructed 3D shape generated from a single image and new synthesized poses of the same person at arbitrary angles. For quantitative comparisons, we compare our synthesized results against 3D scanned data and also perform face recognition using synthesized images generated from a single enrollment frontal image. We obtain promising results for handling pose and expression changes based on the proposed method.

3-D Generic Elastic Models for Fast and Texture Preserving 2-D Novel Pose Synthesis

This paper provides an in-depth analysis on face shape alignment for pose insensitive face recognition. The dissimilarity between two face images can be modeled as the difference in intensity between these two images, obtained by warping these faces onto the same shape. In order to achieve this, we must first align both face images independently to obtain a sparse 2-D shape representation. We achieve this by using a Combination of ASMs and AAMs (CASAAMs). We then exchange these two shapes and obtain new intensity (texture) faces based on these exchanged shapes. This allows us to align the two faces with increased pixel-level correspondence while simultaneously achieving a certain degree of pose correction. In order to account for large pose variation, it becomes necessary to model the underlying 3-D face structure for the synthesis of novel 2-D poses. However, in many real-world scenarios, only a single image of the subject is provided and acquisition of the 3-D model is not always feasible. To tackle this common real-world scenario, we propose a novel approach for modeling faces, called 3D Generic Elastic Model (3D-GEM), which can be deformed from a single 2-D image. Our analysis shows that 3-D depth information of human faces does not dramatically change across people, indicating that precise depth information of a person is not needed to generate useful novel 2-D poses. This is a significant discovery that makes our method feasible. We thus demonstrate that our 3-D face model can be efficiently produced by using a generic depth model which can be elastically deformed based on input facial features. This face model can then be rotated in 3-D in order to synthesize any arbitrary 2-D facial pose. Experimental results show that 3-D faces modeled by our proposed work effectively handle large 3-D pose changes in face alignment and they can be used for achieving pose tolerant face recognition. We also provide comparative results of face synthesis obtained by an actual 3-D face scanner and our approach, showing our proposed modeling approach is both effective and efficient.

Unconstrained Pose-Invariant Face Recognition Using 3D Generic Elastic Models

Classical face recognition techniques have been successful at operating under well-controlled conditions; however, they have difficulty in robustly performing recognition in uncontrolled real-world scenarios where variations in pose, illumination, and expression are encountered. In this paper, we propose a new method for real-world unconstrained pose-invariant face recognition. We first construct a 3D model for each subject in our database using only a single 2D image by applying the 3D Generic Elastic Model (3D GEM) approach. These 3D models comprise an intermediate gallery database from which novel 2D pose views are synthesized for matching. Before matching, an initial estimate of the pose of the test query is obtained using a linear regression approach based on automatic facial landmark annotation. Each 3D model is subsequently rendered at different poses within a limited search space about the estimated pose, and the resulting images are matched against the test query. Finally, we compute the distances between the synthesized images and test query by using a simple normalized correlation matcher to show the effectiveness of our pose synthesis method to real-world data. We present convincing results on challenging data sets and video sequences demonstrating high recognition accuracy under controlled as well as unseen, uncontrolled real-world scenarios using a fast implementation.

Crossed-ratchet effects and domain wall geometrical pinning

The motion of a domain wall in a two dimensional medium is studied taking into account the internal elastic degrees of freedom of the wall and geometrical pinning produced both by holes and sample boundaries. This study is used to analyze the geometrical conditions needed for optimizing crossed ratchet effects in periodic rectangular arrays of asymmetric holes, recently observed experimentally in patterned ferromagnetic films. Geometrical calculations and numerical simulations have been used to obtain the anisotropic critical fields for depinning flat and kinked walls in rectangular arrays of triangles. The aim is to show with a generic elastic model for interfaces how to build a rectifier able to display crossed ratchet effects or effective potential landscapes for controlling the motion of interfaces or invasion fronts.

Spectral Classification of Non-Coaxiality for Two-Dimensional Incremental Stress-Strain Response

The present study examines the non-coaxial aspects of incremental material behavior, and attempts to classify the incremental non-coaxiality that relates stress and strain increments. In the solid mechanics literature, non-coaxiality (NC) refers usually to incremental strains and stress states having different principal directions. Departing from conventional non-coaxiality, the analysis investigates the incremental non-coaxiality (INC) of linearized rate-type solids. This study uses the concept of deviatoric second-order work for examining the relations between stability and incremental non-coaxiality. Based on a spectral analysis of the constitutive compliance matrix, it proposes three classifications for distinguishing various degrees of incremental non-coaxiality and stability. These classifications determine the conditions for the existence of incremental coaxiality (i.e., colinearity of stress and strain increments), stability, instability, and stable-instable transition (i.e., positive, negative, or zero second-order deviatoric work). The study illustrates these classifications in the cases of generic elastic and elastoplastic constitutive models. The analysis pertains to two-dimensional cases. Additional research is required to extend the analysis from two to three dimensions.

Symplectic structure for elastic and chiral conducting cosmic string models

This article is based on the covariant canonical formalism and corresponding symplectic structure on phase space developed by Witten, Zuckerman, and others in the context of field theory. After recalling the basic principles of this procedure, we construct the conserved bilinear symplectic current for generic elastic string models. These models describe current carrying cosmic strings evolving in an arbitrary curved background spacetime. Particular attention is paid to the special case of the chiral string for which the worldsheet current is null. Different formulations of the chiral string action are discussed in detail, and as a result the integrability property of the chiral string is clarified.

Dilatonic formulation for conducting cosmic string models

It can be shown how the the introduction of a suitably defined dilatonic auxiliary field, Φ say, makes it possible for the non-linear Lagrangian for a generic elastic string model, of the kind appropriate for representing superconducting cosmic strings, to be converted into a standardised form as the sum of a kinetic term that is just homogeneously quadratic in the relevant scalar phase gradient (as in a simple linear model) together with a potential energy term, V say, that is specified as a generically non-linear function of Φ. The explicit form of this function is derived for various noteworthy examples, of which the most memorable is that of the transonic string model, as characterised by a given mass scale, m say, for which this potential energy density will be expressible in terms of the zero current limit value Φ0 of Φ by

Dilatonic formulation for conducting cosmic string models

AbstractIt can be shown how the the introduction of a suitably defined dilatonic auxiliary field, Φ say, makes it possible for the non‐linear Lagrangian for a generic elastic string model, of the kind appropriate for representing superconducting cosmic strings, to be converted into a standardised form as the sum of a kinetic term that is just homogeneously quadratic in the relevant scalar phase gradient (as in a simple linear model) together with a potential energy term, V say, that is specified as a generically non‐linear function of Φ. The explicit form of this function is derived for various noteworthy examples, of which the most memorable is that of the transonic string model, as characterised by a given mass scale, m say, for which this potential energy density will be expressible in terms of the zero current limit value Φ0 of Φ by