Access Control of Cloud Computing Using Rapid Face and Fingerprint Identification

Chang, Bao Rong; Tsai, Hsiu-Fen; Chen, Chi-Ming; Huang, Chien‐Feng

doi:10.1109/ibica.2011.49

Cited by 17 publications

(9 citation statements)

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“…Afterward QT/Embedded gets through the kernel driver to activate graphic function. Cloud computing here can perform rapid fingerprint identification and facial recognition in order to fulfill the access control system [21]. We will see whether or not a quick response to client is confirmed.…”

Section: B Establishing Thin Client and Installing Access Controlmentioning

confidence: 99%

Access-controlled video/voice over IP in Hadoop system with BPNN intelligent adaptation

Chang

Tsai

Lin

et al. 2012

2012 International Conference on Information Security and Intelligent Control

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In this paper a real-time video/voice over IP (VVoIP) applications has implemented in Hadoop cloud computing system and it is denoted CLC-IHU. It really outperforms the previous VVoIP using P2P connection (called SCTP-IHU) due to the easyto-use and high-performance on video phone call. User does not need to know what is a real IP and web interface achieves interaction by adopt TCP-based RMTP instead of PR-SCTP so that CLC-IHU scheme reduces computation load and power consumption dramatically at thin clients. We employed a backpropagation neural network (BPNN) to tune key factors appropriately for adapting handoff and analyzing network traffic at any time. As a result it takes about 1.631 sec for the seamless handoff between base stations under mobile wireless network. In access control for preventing illegal intrusions from the outside of the cloud, the rapid facial recognition and fingerprint identification via cloud computing has been done successfully within 2.2 seconds to identify the exact subject.

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Section: B Establishing Thin Client and Installing Access Controlmentioning

confidence: 99%

Access-controlled video/voice over IP in Hadoop system with BPNN intelligent adaptation

Chang

Tsai

Lin

et al. 2012

2012 International Conference on Information Security and Intelligent Control

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“…When users sign-in, it will collect users' biometric features with capturing devices at client side as the evidence of legal or illegal sign-in [16]. 6.…”

Section: In-cloud Erp and Access Control Authenticationmentioning

confidence: 99%

An in-cloud enterprise resource planning system with high availability and access control authentication

Chang

Tsai

et al. 2014

2014 International Conference on Information Science, Electronics and Electrical Engineering

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The service-oriented hosts in enterprises like ERP system have always encountered the crucial problem of unexpected down-time or system failure that will cause data loss and system termination. Failover is a challenge issue that it cannot be done successfully between physical hosts. Therefore this paper introduces high availability architecture to build an in-cloud Enterprise Resources Planning together with access control authentication deployed in the virtual machine cluster, which can resolve the problems mentioned above. Access control authentication has been designed in the cloud computing system to prevent the service-oriented hosts form external fraud or intrusion. As a result, the approach proposed in this paper outperforms two well-known benchmark ERP systems, in-house ECC 6.0 and in-cloud ByDesign.

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“…Examples of large-scale fingerprint systems could be found in the U.S. government including the US-VISIT’s IDENT program [3] and the FBI’s IAFIS services [4]. Recently, applications of fingerprint identification have been also combined with cloud computing technology [5]. …”

Section: Introductionmentioning

confidence: 99%

A Support Vector Machine Approach for Truncated Fingerprint Image Detection from Sweeping Fingerprint Sensors

Chen

Pai

Cheng³

2015

Sensors

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A sweeping fingerprint sensor converts fingerprints on a row by row basis through image reconstruction techniques. However, a built fingerprint image might appear to be truncated and distorted when the finger was swept across a fingerprint sensor at a non-linear speed. If the truncated fingerprint images were enrolled as reference targets and collected by any automated fingerprint identification system (AFIS), successful prediction rates for fingerprint matching applications would be decreased significantly. In this paper, a novel and effective methodology with low time computational complexity was developed for detecting truncated fingerprints in a real time manner. Several filtering rules were implemented to validate existences of truncated fingerprints. In addition, a machine learning method of supported vector machine (SVM), based on the principle of structural risk minimization, was applied to reject pseudo truncated fingerprints containing similar characteristics of truncated ones. The experimental result has shown that an accuracy rate of 90.7% was achieved by successfully identifying truncated fingerprint images from testing images before AFIS enrollment procedures. The proposed effective and efficient methodology can be extensively applied to all existing fingerprint matching systems as a preliminary quality control prior to construction of fingerprint templates.

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Access Control of Cloud Computing Using Rapid Face and Fingerprint Identification

Cited by 17 publications

References 0 publications

Access-controlled video/voice over IP in Hadoop system with BPNN intelligent adaptation

Access-controlled video/voice over IP in Hadoop system with BPNN intelligent adaptation

An in-cloud enterprise resource planning system with high availability and access control authentication

A Support Vector Machine Approach for Truncated Fingerprint Image Detection from Sweeping Fingerprint Sensors

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