Fuzzy Edge Detection in Biometric Systems

Local and global information affect cooperation in networked Prisoner’s dilemma games

Multi-view clustering can mine the underlying structure of multi-view data and has attracted increasing attention. Most existing multi-view clustering methods either construct the similarity matrix from the global level through self-representation learning or construct the similarity matrix from the local level through graph learning. Spectral clustering method can be used to yield the clustering results based on the similarity matrix. However, the similarity matrix that only considers global information or local information is not robust. Moreover, separating the similarity matrix learning and clustering as two steps may lead to sub-optimal clustering results. To address these issues, we propose in this paper, a multi-view subspace clustering with local and global information (MVSCLG) method. Our method combines the self-representation learning and graph learning to learn a similarity matrix with global and local information, and simultaneously utilizes the spectral decomposition and the spectral rotation techniques to yield the clustering results. We also develop an effective optimization algorithm to solve the resulting optimization problem. The effectiveness and superiority of this method are verified on four multi-view benchmark data sets.

Most works on financial forecasting use information directly associated with individual companies (e.g., stock prices, news on the company) to predict stock returns for trading. We refer to such company-specific information as local information. Stock returns may also be influenced by global information (e.g., news on the economy in general), and inter-company relationships. Capturing such diverse information is challenging due to the low signal-to-noise ratios, different time-scales, sparsity and distributions of global and local information from different modalities. In this paper, we propose a model that captures both global and local multimodal information for investment and risk management-related forecasting tasks. Our proposed Guided Attention Multimodal Multitask Network (GAME) model addresses these challenges by using novel attention modules to guide learning with global and local information from different modalities and dynamic inter-company relationship networks. Our extensive experiments show that GAME outperforms other state-of-the-art models in several forecasting tasks and important real-world application case studies.

Vibration-based damage detection methods have inherent uncertainties due to the perturbation of measurement noise, modeling errors, and environmental changes, such as variations in temperature. However, other measured local information, including the static strain and deflection measured by the structural health monitoring system, have not been taken full advantage of. The integration of such global and local information has the potential to lead to more accurate structural damage detection. This paper proposes a method for integrating the global and local information for structural damage detection based on Bayesian theory. First, the Bayesian probability model associated with natural frequencies, displacement mode shapes, and strain modes is developed. In this model, the local strain information is also used as an input. Second, to reduce the model's computational cost in complex structures, the strain energy damage index is employed to determine the potential damage ranges. Finally, the exact damage elements are detected by a proposed sequence elimination method. Numerical simulations on a 14-bay planar rigid structure and the experiment on a 20-bay rigid truss are carried out to demonstrate the effectiveness of the proposed method while considering model uncertainty and measurement noise. The results show that the proposed damage detection method can increase the model's identification accuracy and decrease its misjudgment rate, compared with the method that only uses global information.

The generalization challenges faced by neural methods in solving the Travelling Salesman Problem (TSP) have attracted substantial attention. Current neural approaches predominantly rely on Deep Neural Networks with global receptive fields to capture global features, often overlooking the critical role of local topological information. To address this limitation, we propose a Global & Local Encoder (G&L-Encoder) that efficiently integrates local and global information within the feature space. The G-Encoder, with global receptive field, takes charge of capturing global features and facilitating the fusion of global and local information, while the L-Encoder, with its local neighborhood receptive field, extracts finer-grained features specific to each node's neighborhood, serving as an essential complement to the global one. Furthermore, we propose a simple yet efficient supervised learning framework, leveraging the recursive optimality inherent in optimal solutions to dig into their implicit knowledge. Extensive experiments on the TSP demonstrate that our method significantly improves generalization performance and achieves state-of-the-art results on medium-scale TSP tasks. Our source code is available at https://github.com/bxx-seu/GL-POMO.

After continuous development, many researchers are exploring how to better utilize global and local information in single image super-resolution (SISR). Various methods based on convolutional neural network (CNN) and Transformer structures have emerged, but few studies have mentioned how to combine these two parts of information. We study the use of self-attention mechanism to integrate local and global information, aiming to make the model better balance the weights of the two parts of information. At the same time, in order to avoid the huge amount of computation brought by Transformer, we use the selective state space model VMamba to extract global information to achieve the effect of reducing computational complexity and lightweight network. Based on the above situation, we propose a High-frequency Mamba Network (HFMN) for SISR, which includes the local high-frequency extraction module Local High-Frequency Feature Block (LHFB), the global feature extraction module Mamba-Based Attention Block (MAB) based on VMamba, and the dual attention fusion module Dual-information Interactive Attention Block (DIAB). It can better incorporate local and global information and has linear complexity in the global feature extraction branch. Experiments on multiple benchmark datasets demonstrate that the network outforms recent SOTA methods in SISR while using fewer parameters. All codes are available at https://github.com/taoWuuu/HFMN .

Friction ridge impression from the areas of the fingers or hands that are not from the fingertips are called non-distal prints, which include palm prints, middle and proximal phalangeal prints, and interphalangeal prints. The presence of these fingerprints is less compared to distal print however, once retrieved from the scene of crime allow the identification of the individual who committed the crime. Therefore, this case report presents a burglary case where phalangeal prints from the finger retrieved from the site of crime matched the prints collected from the suspect. This case report also goes through advancements brought by Automated Biometric Identification System (ABIS) such as enhancing low-quality latent fingerprint, real-time picture and matching, and integration of other biometric systems such as Iris scan, Facial recognition.

This research discusses the evaluation of biometric systems that are designed to process hundreds to tens of thousands of individuals in short time spans. We propose a method for evaluating a system's performance across capture attempts for the purpose of identifying characteristics that are advantageous in these high-throughput environments. We also present a novel modification to the traditionally accepted biometric performance metrics of failure-to-acquire, and true-match rate. Namely, this paradigm shift holds that these metrics are a function of time and, as such, vary with the time available for a biometric system to interact with a user. This research demonstrates the utility of these time-based metrics in evaluating the performance of multiple, commercially available, high-throughput systems. We show that different biometric systems have notably different time-based performance curves using a corpus of data collected during the 2018 Department of Homeland Security, Science and Technology Directorate (DHS S&T) Biometric Technology Rally. These curves and the deviations between them are useful when quantifying the suitability of a technology, evaluated via scenario testing, for deployment in an operational environment where the throughput of the target population is a key performance parameter.

In this paper, we propose a congestion-aware routing algorithm based on Dual Reinforcement Q-routing. In this method, local and global congestion information of the network is provided for each router, utilizing learning packets. This information should be dynamically updated according to the changing traffic conditions in the network. For this purpose, a congestion detection method is presented to measure the average of free buffer slots in a specific time interval. This value is compared with maximum and minimum threshold values and based on the comparison result, the learning rate is updated. If the learning rate is a large value, it means the network gets congested and global information is more emphasized than local information. In contrast, local information is more important than global when a router receives few packets in a time interval. Experimental results for different traffic patterns and network loads show that the proposed method improves the network performance compared with the standard Q-routing, DRQ-routing, and Dynamic XY-routing algorithms.

Biometrics using biological or behavioral features to authenticate a person has attracted much attention as a new authentication approach against traditional ones such as key, password, etc. Biometrics technologies provide us better security and greater convenience than traditional person authentication technologies such as key, password and card. Biometric systems with cameras involve the risk of spoofing. For example in face recognition systems, when a malicious person turns a printed face photo of an authenticated user to a camera, a face recognition system may accept the malicious person as the authenticated user. To address the above problem, liveness detection is important to develop secure biometric recognition systems. This paper presents a liveness detection method using deep learning for face recognition systems.

Template-Protection-Techniken stellen eine wichtige Ergänzung zu biometrischen Systemen, um das gewünschte Sicherheitsniveau zu erreichen und die Anforderungen an den Schutz der Privatsphäre zu erfüllen. Diese Techniken ermöglichen Erneuerung und Widerruf der Referenzendaten in biometrischen Systemen. In dieser Arbeit wird ein systematisches Evaluierungsframework entwickelt, das sich an die wesentlichen Kriterien und Anforderungen an biometrische Template-Protection-Verfahren festhält. Seine Anwendbarkeit wird durch die Analyse der verschiedenen Algorithmen demonstriert. Die in dieser Arbeit präsentierte Evaluierung ist grundlegend für eine vollständige Analyse. Darüber hinaus ermöglicht es einen Nachweis der Sicherheit und Einhaltung der Privatsphäre. Es schafft eine Basis für Zertifizierung und Benchmark der Template-Protection-Verfahren.

Biometric systems have been using widely and these systems have improved substantially which are being used for persons Authentication and verification. This Biometrics is playing a key role in Personal, National and Global security. The main approach of this paper is to tell the importance of Biometric systems. Different types of biometric systems have been used these days. These biometric systems are iris recognition, palm vein biometrics, voice recognition, face recognition. The main threat to all these Biometric systems is Spoofing. Facial recognition biometric system is being widely used when compared to other biometric systems. Different types of facial spoofing include mask attack; photo attack & video attack and Different methods used to detect Face spoofing are discussed in this paper. These face spoofing detection techniques include the use of LBP, DMD, SVM, PReLU, IDA, LFHOG and CNN. The Other Techniques Include a Combination of LBP, CNN, and IDA. However, the results strongly prove that the Implementation of the above techniques will detect Face spoofing.

Energy-efficient topology control in wireless ad hoc networks with selfish nodes

Most current infrared small target detection methods attempt to fuse local and global information by using single-scale inputs and creating a multi-scale feature pyramid during network feeding forwards. Further to this, our research finds that using high-resolution inputs can improve recall, while low-resolution inputs improve precision. Nevertheless, solely focusing on global or local information can result in missing target and false alarm. To address these issues, we propose the BPR-Net to balance precision and recall via a novel multi-scale attention mechanism, which combines semantic and shallow features of multi-scale inputs. We first scale the input image into multiple images with varying resolutions and feed them into the network. In the encoder, Scale Fusion Module (SFM) fuses features from corresponding images of different resolutions. In the decoder, a Channel Fusion Module (CFM) fuses useful information from multiple channels. Furthermore, a Wavelet Transform cross-layer skip Layer (WTL) is employed to enhance the interaction between decoder layers for more effective multi-scale feature fusion. Experimental results demonstrate that our approach achieves a balance between recall and precision and yields state-of-the-art performance on challenging benchmarks including Sirst, MDvsFA, and SIATD. Notably, our approach achieves an F1 score of 0.9409 on the challenging benchmark SIATD, surpassing the state-of-the-art method by 16.7%.

Lumen segmentation of the carotid artery is an important preprocessing step with clinical application, because it facilitates subsequent analysis, including stenosis grading, the detection and quantification of plaque components in the vessel wall. But it is a challenging task owing to the low and varying contrast between the surrounding tissues. Plus, intensity inhomogeneity often occurs in medical images from different modalities, which causes undesirable side effects when the traditional Chan-Vese (CV) model is applied. In order to overcome the intensity inhomogeneity problem we improve the CV model by adding a local fitting term, which incorporates both local information and global information. We also introduce a penalizing energy to avoid the time-consuming re-initialization procedure in traditional level set method.We propose and validate a semi-automatic method for lumen segmentation of carotid artery in computed tomography angiography(CTA). First we manually select a seedpoint on the lumen area, then the segmentation is automatically obtained using a level set. By combining both global and local statistical information, we manage to overcome the inhomogeneous intensity distribution in the CTA images and provide more satisfying segmentation result than CV model. Experiments on carotid artery CTA data have demonstrated the efficiency and accuracy of our model, in addition our model is also less sensitive to the location of the initial contour. Therefore our method has the potential to replace the manual procedure of lumen segmentation in CTA, which is of great value for doctors with clinical applications.