scholarly journals An Improved Multispectral Palmprint Recognition System Using Autoencoder with Regularized Extreme Learning Machine

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Abdu Gumaei ◽  
Rachid Sammouda ◽  
Abdul Malik S. Al-Salman ◽  
Ahmed Alsanad
Keyword(s):  
Feature Extraction ◽  
Extreme Learning Machine ◽  
State Of The Art ◽  
Region Of Interest ◽  
Recognition System ◽  
The State ◽  
Palmprint Recognition ◽  
Novel Approach ◽  
And Performance ◽  
Learning Machine

Multispectral palmprint recognition system (MPRS) is an essential technology for effective human identification and verification tasks. To improve the accuracy and performance of MPRS, a novel approach based on autoencoder (AE) and regularized extreme learning machine (RELM) is proposed in this paper. The proposed approach is intended to make the recognition faster by reducing the number of palmprint features without degrading the accuracy of classifier. To achieve this objective, first, the region of interest (ROI) from palmprint images is extracted by David Zhang’s method. Second, an efficient normalized Gist (NGist) descriptor is used for palmprint feature extraction. Then, the dimensionality of extracted features is reduced using optimized AE. Finally, the reduced features are fed to the RELM for classification. A comprehensive set of experiments are conducted on the benchmark MS-PolyU dataset. The results were significantly high compared to the state-of-the-art approaches, and the robustness and efficiency of the proposed approach are revealed.

scholarly journals Feature Extraction Based Face Recognition Using Extreme Learning Machine (ELM)

2016 ◽  
pp. 80-83
Author(s):  
NAGABHAIRAVA VENKATA SIDDARTHA ◽  
MOHAMMAD UMAR ◽  
NABANKUR SEN ◽  
P. KRISHNA PRASAD
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Extreme Learning Machine ◽  
Credit Card ◽  
Recognition System ◽  
Extraction Process ◽  
Biometric Identification ◽  
Face Recognition System ◽  
The Face ◽  
Learning Machine

In recent years, Face recognition becomes one of the popular biometric identification systems used in identifying or verifying individuals and matching it against library of known faces. Biometric identification is an actively growing area of research and used in electronic commerce, electronic banking, electronic passports, electronic licences and security applications. Face recognition finds its application in wide variety of areas like criminal identification, human - computer interaction, security systems, credit- card verification, teleconference, image and film processing. This paper suggests an automated face recognition system which extracts the features from the face. Feature extraction process includes locating the position of eyes, nostrils and mouth and determining the distances between those regions. From the extracted features, a database is created for known individuals. A virtual neural network is created based on Extreme Learning Machine (ELM).

scholarly journals Density Guarantee on Finding Multiple Subgraphs and Subtensors

2021 ◽  
Vol 15 (5) ◽  
pp. 1-32
Author(s):  
Quang-huy Duong ◽  
Heri Ramampiaro ◽  
Kjetil Nørvåg ◽  
Thu-lan Dam
Keyword(s):  
Lower Bound ◽  
State Of The Art ◽  
The State ◽  
The Other ◽  
Exact Methods ◽  
Practical Solution ◽  
Novel Approach ◽  
Wide Range ◽  
Real World Datasets ◽  
Tensor Data

Dense subregion (subgraph & subtensor) detection is a well-studied area, with a wide range of applications, and numerous efficient approaches and algorithms have been proposed. Approximation approaches are commonly used for detecting dense subregions due to the complexity of the exact methods. Existing algorithms are generally efficient for dense subtensor and subgraph detection, and can perform well in many applications. However, most of the existing works utilize the state-or-the-art greedy 2-approximation algorithm to capably provide solutions with a loose theoretical density guarantee. The main drawback of most of these algorithms is that they can estimate only one subtensor, or subgraph, at a time, with a low guarantee on its density. While some methods can, on the other hand, estimate multiple subtensors, they can give a guarantee on the density with respect to the input tensor for the first estimated subsensor only. We address these drawbacks by providing both theoretical and practical solution for estimating multiple dense subtensors in tensor data and giving a higher lower bound of the density. In particular, we guarantee and prove a higher bound of the lower-bound density of the estimated subgraph and subtensors. We also propose a novel approach to show that there are multiple dense subtensors with a guarantee on its density that is greater than the lower bound used in the state-of-the-art algorithms. We evaluate our approach with extensive experiments on several real-world datasets, which demonstrates its efficiency and feasibility.

A Survey of Real-Time Industrial Wireless LAN Research

2015 ◽  
Vol 738-739 ◽  
pp. 1105-1110 ◽  
Author(s):  
Yuan Qing Qin ◽  
Ying Jie Cheng ◽  
Chun Jie Zhou
Keyword(s):  
Real Time ◽  
Future Development ◽  
Wireless Lan ◽  
State Of The Art ◽  
The State ◽  
Local Networks ◽  
The Real ◽  
Time Requirements ◽  
And Performance ◽  
Fundamental Mechanism

This paper mainly surveys the state-of-the-art on real-time communicaton in industrial wireless local networks(WLANs), and also identifys the suitable approaches to deal with the real-time requirements in future. Firstly, this paper summarizes the features of industrial WLANs and the challenges it encounters. Then according to the real-time problems of industrial WLAN, the fundamental mechanism of each recent representative resolution is analyzed in detail. Meanwhile, the characteristics and performance of these resolutions are adequately compared. Finally, this paper concludes the current of the research and discusses the future development of industrial WLANs.

scholarly journals The State-of-the-Art Technology of Currency Identification

2017 ◽  
Vol 9 (3) ◽  
pp. 58-72 ◽  
Author(s):  
Guangyu Wang ◽  
Xiaotian Wu ◽  
WeiQi Yan
Keyword(s):  
Feature Extraction ◽  
New Zealand ◽  
Comparative Study ◽  
State Of The Art ◽  
The State ◽  
Classification Algorithms ◽  
Security Issue ◽  
The Public ◽  
Us Dollar ◽  
Chinese Yuan

The security issue of currency has attracted awareness from the public. De-spite the development of applying various anti-counterfeit methods on currency notes, cheaters are able to produce illegal copies and circulate them in market without being detected. By reviewing related work in currency security, the focus of this paper is on conducting a comparative study of feature extraction and classification algorithms of currency notes authentication. We extract various computational features from the dataset consisting of US dollar (USD), Chinese Yuan (CNY) and New Zealand Dollar (NZD) and apply the classification algorithms to currency identification. Our contributions are to find and implement various algorithms from the existing literatures and choose the best approaches for use.

The State-of-the-Art Technology of Currency Identification

2018 ◽  
pp. 252-269
Author(s):  
Guangyu Wang ◽  
Xiaotian Wu ◽  
WeiQi Yan
Keyword(s):  
Feature Extraction ◽  
New Zealand ◽  
Comparative Study ◽  
State Of The Art ◽  
The State ◽  
Classification Algorithms ◽  
Security Issue ◽  
The Public ◽  
Us Dollar ◽  
Chinese Yuan

The security issue of currency has attracted awareness from the public. De-spite the development of applying various anti-counterfeit methods on currency notes, cheaters are able to produce illegal copies and circulate them in market without being detected. By reviewing related work in currency security, the focus of this paper is on conducting a comparative study of feature extraction and classification algorithms of currency notes authentication. We extract various computational features from the dataset consisting of US dollar (USD), Chinese Yuan (CNY) and New Zealand Dollar (NZD) and apply the classification algorithms to currency identification. Our contributions are to find and implement various algorithms from the existing literatures and choose the best approaches for use.

Cloud Monitoring

2012 ◽  
pp. 97-116
Author(s):  
Peer Hasselmeyer ◽  
Gregory Katsaros ◽  
Bastian Koller ◽  
Philipp Wieder
Keyword(s):  
State Of The Art ◽  
The State ◽  
Cloud Environment ◽  
State Health ◽  
Cloud Monitoring ◽  
Cloud Environments ◽  
And Performance ◽  
Cloud Infrastructures ◽  
Different Levels ◽  
The Way

The management of the entire service landscape comprising a Cloud environment is a complex and challenging venture. There, one task of utmost importance, is the generation and processing of information about the state, health, and performance of the various services and IT components, something which is generally referred to as monitoring. Such information is the foundation for proper assessment and management of the whole Cloud. This chapter pursues two objectives: first, to provide an overview of monitoring in Cloud environments and, second, to propose a solution for interoperable and vendor-independent Cloud monitoring. Along the way, the authors motivate the necessity of monitoring at the different levels of Cloud infrastructures, introduce selected state-of-the-art, and extract requirements for Cloud monitoring. Based on these requirements, the following sections depict a Cloud monitoring solution and describe current developments towards interoperable, open, and extensible Cloud monitoring frameworks.

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