Component-based Representation Research Articles

Sparse Individual Low-Rank Component Representation for Face Recognition in the IoT-Based System

The performance of face recognition has been greatly improved by deep neural network algorithms when a dataset is large. However, when face data are insufficient as in practical Internet of Things (IoT) applications and captured by IoT devices under the same intrasubject variation, both data quantity and quality bring big challenges to construct a model or representation, and most of the time it becomes infeasible to build a deep neural network model. This work proposes a sparse individual low-rank component-based representation (SILR) such that the representation of testing images can be based on individual subjects’ low-rank component. Theoretically, we put the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l_{2}$ </tex-math></inline-formula> -norm constraint on intrasubject coefficients to represent testing images, thus making intrasubject coefficients dense. Hence, we alleviate the impact of an undersampled training dataset and its same intersubject variation on classification performance. We solve a convex minimization problem in polynomial time via an augmented lagrange multiplier scheme to get the solution of SILR. The scheme can reduce the influences from the same intersubject variation and contribute to an accurate recognition of the undersampled training dataset. We adopt sparse individual low-rank component representation and minimum reconstruction residual to recognize testing images. Extensive results on various databases show that SILR outperforms the other state-of-the-art methods for face recognition.

A model for the visualization and analysis of elevator traffic

ABSTRACTAnalysis of elevator traffic in high rise buildings is critical to the performance evaluation of elevator group control systems (EGCS). Elevator dispatching methods or parking algorithms in an EGCS can be designed or modified according to analyses of traffic flow. However, interpretation of traffic flow based solely on numerical data may not be explicit and transparent for EGCS experts as well as for other non-expert building administration. In this study, we present a model for visualization and analysis of elevator traffic. First, we present an alternative approach for traffic analysis which we call route visualization. In the proposed approach, we initially decompose elevator traffic into its component parts and investigate each component independently. Then, using superposition of components we obtain a reconstructed model of overall traffic. This modeling approach provides component-based traffic analysis and representation of routes with intensities through data visualization. In the second part we introduce a multi-dimensional analysis of time parameters in ECGS. This approach provides a comparative analysis of several control algorithms such as dispatch or park algorithms for different combinations of traffic components.

A Study on various state of the art of the Art Face Recognition System using Deep Learning Techniques

Considering the existence of very large amount of available data repositories and reach to the very advanced system of hardware, systems meant for facial identification ave evolved enormously over the past few decades. Sketch recognition is one of the most important areas that have evolved as an integral component adopted by the agencies of law administration in current trends of forensic science. Matching of derived sketches to photo images of face is also a difficult assignment as the considered sketches are produced upon the verbal explanation depicted by the eye witness of the crime scene and may have scarcity of sensitive elements that exist in the photograph as one can accurately depict due to the natural human error. Substantial amount of the novel research work carried out in this area up late used recognition system through traditional extraction and classification models. But very recently, few researches work focused on using deep learning techniques to take an advantage of learning models for the feature extraction and classification to rule out potential domain challenges. The first part of this review paper basically focuses on deep learning techniques used in face recognition and matching which as improved the accuracy of face recognition technique with training of huge sets of data. This paper also includes a survey on different techniques used to match composite sketches to human images which includes component-based representation approach, automatic composite sketch recognition technique etc.

Sparse Low-Rank Component-Based Representation for Face Recognition With Low-Quality Images

Sparse-representation-based classification (SRC) has been showing a good performance for face recognition in recent years. But SRC is not good at face recognition with low quality images (e.g., disguised, corrupted, occluded, and so on) which often appear in practical applications. To solve the problem, in this paper, we propose a novel SRC-based method for face recognition with low quality images named sparse low-rank component-based representation (SLCR). In SLCR, we utilize low-rank matrix recovery on the training data set to obtain low-rank components and non-low-rank components, which are used to construct the dictionary. The new dictionary is capable of describing facial features better, especially for low quality face samples. Furthermore, the minimum class-wise reconstruction residual is used as the recognition rule, leading to a substantial improvement on the proposed SLCR’s performance. Extensive experiments on benchmark face databases demonstrate that the proposed method is consistently superior to other sparse-representation-based approaches for face recognition with low quality images.

Composite components-based face sketch recognition

Abstract Composite face sketch recognition is a challenging problem in face recognition, which is important for law enforcement. Considering composite sketch is generated from facial components, a component-based representation approach (CBR) was proposed to match composite sketch to photos recently. They extract multi-scale local binary pattern (MLBP) feature from every component and use the fusion of four most discriminative components as the final matching score, ignoring the inherent structure of composite sketch and the fusion does not take the full components’ information into consideration. This paper presents a novel composite sketch recognition method by extracting scale-invariant feature transform (SIFT) feature and histogram of oriented gradient (HOG) feature from components, fusing different features at score level, combining facial components with linear function. In our proposed method, feature fusion could extract local texture and structure feature from composite sketches. The linear combination not only reserves complete component information but also protrudes different components contribution to the holistic face image. In addition, we show the detailed procedure of composite sketch generation experiment, helping build related face database. Experiments on several public composite sketch databases (including a newly published UoM-SGFS database) demonstrate that our proposed method achieves superior performance compared with state-of-the-art methods.

Matching Composite Sketches to Facial Photos using Component-based Approach

Face recognition has wide potential applications in many computer area like face identification, face verification, criminal investigation. The face images can be represented by various representation approaches. Common approaches for representations includes component- based, analytic, patch-based and holistic. Also various researches on sketch recognition which focused on matching sketches drawn by professional artist based on a verbal description of the subject's appearances provided by an eyewitness. In the proposed system composite sketches are synthesized using one of the several facial composite software systems which are publically available. We propose a component-based representation (CBR) approach to measure the similarity score between a composite sketch and mugshot photograph. Using an active shape model (ASM), first automatically detect facial landmarks in composite sketches and face photos. In propose system instead of comparing whole image at the same time, facial components from dataset will be compare for matching. Depending on the component matching images will be display in sorted order. Keywords-Component-based face representation, composite sketch, faces recognition, forensic sketch, heterogeneous face recognition, modality gap.

Class-Specific Kernel Fusion of Multiple Descriptors for Face Verification Using Multiscale Binarised Statistical Image Features

This paper addresses face verification in unconstrained settings. For this purpose, first, a nonlinear binary class-specific kernel discriminant analysis classifier (CS-KDA) based on spectral regression kernel discriminant analysis is proposed. By virtue of the two-class formulation, the proposed CS-KDA approach offers a number of desirable properties such as specificity of the transformation for each subject, computational efficiency, simplicity of training, isolation of the enrolment of each client from others and increased speed in probe testing. Using the proposed CS-KDA approach, a regional discriminative face image representation based on a multiscale variant of the binarized statistical image features is proposed next. The proposed component-based representation when coupled with the dense pixel-wise alignments provided by a symmetric MRF matching model reduces the sensitivity to misalignments and pose variations, gauging the similarity more effectively. Finally, the discriminative representation is combined with two other effective image descriptors, namely the multiscale local binary patterns and the multiscale local phase quantization histograms via a kernel fusion approach to further enhance system accuracy. The experimental evaluation of the proposed methodology on challenging databases demonstrates its advantage over other methods.

Behaviour of open beam-to-tubular column angle connections under combined loading conditions

This paper examines the behaviour of two types of practical open beam-to-tubular column connection details subjected to combined moment, axial and/or shear loads. Detailed continuum finite element models are developed and validated against available experimental results, and extended to deal with flexural, axial and shear load interactions. A numerical investigation is then carried out on the behaviour of selected connections with different stiffness and strength characteristics under various load combination scenarios. The influence of applied levels of axial tensile or compressive loads on the bending stiffness and capacity is examined and discussed. Additionally, the interaction effects between shear forces and co-existing bending and axial loads are examined and shown to be comparatively insignificant in terms of stiffness and capacity in most cases. It is also shown that the range of connections considered in this paper can provide rotational ductility levels in excess of those required under typical design scenarios. Based on these findings, a simplified component-based representation is proposed and described, and its ability to represent the connection response under combined loading is verified using results from detailed numerical simulations.

Component-Based Representation in Automated Face Recognition

This paper presents a framework for component-based face alignment and representation that demonstrates improvements in matching performance over the more common holistic approach to face alignment and representation. This work is motivated by recent evidence from the cognitive science community demonstrating the efficacy of component-based facial representations. The component-based framework presented in this paper consists of the following major steps: 1) landmark extraction using Active Shape Models (ASM), 2) alignment and cropping of components using Procrustes Analysis, 3) representation of components with Multiscale Local Binary Patterns (MLBP), 4) per-component measurement of facial similarity, and 5) fusion of per-component similarities. We demonstrate on three public datasets and an operational dataset consisting of face images of 8000 subjects, that the proposed component-based representation provides higher recognition accuracies over holistic-based representations. Additionally, we show that the proposed component-based representations: 1) are more robust to changes in facial pose, and 2) improve recognition accuracy on occluded face images in forensic scenarios.

Matching Composite Sketches to Face Photos: A Component-Based Approach

The problem of automatically matching composite sketches to facial photographs is addressed in this paper. Previous research on sketch recognition focused on matching sketches drawn by professional artists who either looked directly at the subjects (viewed sketches) or used a verbal description of the subject's appearance as provided by an eyewitness (forensic sketches). Unlike sketches hand drawn by artists, composite sketches are synthesized using one of the several facial composite software systems available to law enforcement agencies. We propose a component-based representation (CBR) approach to measure the similarity between a composite sketch and mugshot photograph. Specifically, we first automatically detect facial landmarks in composite sketches and face photos using an active shape model (ASM). Features are then extracted for each facial component using multiscale local binary patterns (MLBPs), and per component similarity is calculated. Finally, the similarity scores obtained from individual facial components are fused together, yielding a similarity score between a composite sketch and a face photo. Matching performance is further improved by filtering the large gallery of mugshot images using gender information. Experimental results on matching 123 composite sketches against two galleries with 10,123 and 1,316 mugshots show that the proposed method achieves promising performance (rank-100 accuracies of 77.2% and 89.4%, respectively) compared to a leading commercial face recognition system (rank-100 accuracies of 22.8% and 52.0%) and densely sampled MLBP on holistic faces (rank-100 accuracies of 27.6% and 10.6%). We believe our prototype system will be of great value to law enforcement agencies in apprehending suspects in a timely fashion.

Hierarchical component-based representations for evolving microelectromechanical systems designs

AbstractIn this paper we present a genotype representation method for improving the performance of genetic-algorithm-based optimal design and synthesis of microelectromechanical systems. The genetic algorithm uses a hierarchical component-based genotype representation, which incorporates specific engineering knowledge into the design optimization process. Each microelectromechanical system component is represented by a gene with its own parameters defining its geometry and the way it can be modified from one generation to the next. The object-oriented genotype structures efficiently describe the hierarchical nature typical of engineering designs. They also encode knowledge-based constraints that prevent the genetic algorithm from wasting time exploring inappropriate regions of the search space. The efficiency of the hierarchical component-based genotype representation is demonstrated with surface-micromachined resonator designs.

Multi-objective analysis of a component-based representation within an interactive evolutionary design system

This article describes research relating to a user-centered evolutionary design system that evaluates both engineering and aesthetic aspects of design solutions during early-stage conceptual design. The experimental system comprises several components relating to user interaction, problem representation, evolutionary search and exploration and online learning. The main focus of the article is the evolutionary aspect of the system when using a single quantitative objective function plus subjective judgment of the user. Additionally, the manner in which the user-interaction aspect affects system output is assessed by comparing Pareto frontiers generated with and without user interaction via a multi-objective evolutionary algorithm (MOEA). A solution clustering component is also introduced and it is shown how this can improve the level of support to the designer when dealing with a complex design problem involving multiple objectives. Supporting results are from the application of the system to the design of urban furniture which, in this case, largely relates to seating design.

Computational modular model library of gas turbine

Designing and developing new aircraft engine is time-consuming and expensive. Computational simulation is a promising means for alleviating this cost, but constructing flexible simulation software capable of evaluation of integrated aircraft engine system architectures is hard work. This paper addresses the design of a tool—a generic modular-modeled library of air gas turbine, this library is based on object-oriented technology and hierarchical decomposition and provides a flexible component-based representation for defining, modifying, and simulating the aircraft gas turbine system, subsystem and components. It enables users to customize and extend the framework to add new functionality or adapt the simulation behavior as required, and it allows new models to be composed programmatically or graphically to form more complex models. The model library can be used in steady-state and transient analysis of the aero-engine. It is also a user-friendly, accurate, fast PCbased and easily reusable simulating tool. The advanced object-oriented simulation language Modelica is used to construct this library. Modelica provides a powerful tool to design the library. All of the work described in this paper is developed based upon Dymola/Modelica.

Competitive spiking and indirect entropy minimization of rate code: Efficient search for hidden components

Our motivation, which originates from the psychological and physiological evidences of component-based representations in the brain, is to find neural methods that can efficiently search for structures. Here, an architecture made of coupled parallel working reconstruction subnetworks is presented. Each subnetwork utilizes non-negativity constraint on the generative weights and on the internal representation. ‘Spikes’ are generated within subnetworks via winner take all mechanism. Memory components are modified in order to directly minimize the reconstruction error and to indirectly minimize the entropy of the spike rate distribution, via a combination of a stochastic gradient search and a novel tuning method. This tuning dynamically changes the learning rate: the higher the entropy of the spike rate, the higher the learning rate of the gradient search in the subnetworks. This method effectively reduces the search space and increases the escape probability from high entropy local minima. We demonstrate that one subnetwork can develop localized and oriented components. Coupled networks can discover and sort components into the subnetworks; a problem subject to combinatorial explosion. Synergy between spike code and rate code is discussed.

Computational Simulation of Gas Turbines: Part 1—Foundations of Component-Based Models

Designing and developing new aerospace propulsion systems is time-consuming and expensive. Computational simulation is a promising means for alleviating this cost, but requires a flexible software simulation system capable of integrating advanced multidisciplinary and multifidelity analysis methods, dynamically constructing arbitrary simulation models, and distributing computationally complex tasks. To address these issues, we have developed Onyx, a Java-based object-oriented domain framework for aerospace propulsion system simulation. This paper presents the design of a common engineering model formalism for use in Onyx. This approach, which is based on hierarchical decomposition and standardized interfaces, provides a flexible component-based representation for gas turbine systems, subsystems and components. It allows new models to be composed programmatically or visually to form more complex models. Onyx’s common engineering model also supports integration of a hierarchy of models which represent the system at differing levels of abstraction. Selection of a particular model is based on a number of criteria, including the level of detail needed, the objective of the simulation, the available knowledge, and given resources. The common engineering model approach is demonstrated by developing gas turbine component models which will be used to compose a gas turbine engine model in Part 2 of this paper. [S0742-4795(00)02303-6]

From basis functions to basis fields: vector field approximation from sparse data.

Recent investigations (Poggio and Girosi 1990b) have pointed out the equivalence between a wide class of learning problems and the reconstruction of a real-valued function from a sparse set of data. However, in order to process sensory information and to generate purposeful actions living organisms must deal not only with real-valued functions but also with vector-valued mappings. Examples of such vector-valued mappings range from the optical flow fields associated with visual motion to the fields of mechanical forces produced by neuromuscular activation. In this paper, I discuss the issue of vector-field processing from a broad computational perspective. A variety of vector patterns can be efficiently represented by a combination of linearly independent vector fields that I call "basis fields". Basis fields offer in some cases a better alternative to treating each component of a vector as an independent scalar entity. In spite of its apparent simplicity, such a component-based representation is bound to change with any change of coordinates. In contrast, vector-valued primitives such as basis fields generate vector field representations that are invariant under coordinate transformations.