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Article
Publication date: 3 August 2021

Jianhua Zhang, Shengyong Chen, Honghai Liu and Naoyuki Kubota

Abstract

Details

Industrial Robot: the international journal of robotics research and application, vol. 48 no. 5
Type: Research Article
ISSN: 0143-991X

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Article
Publication date: 7 June 2021

Sixian Chan, Jian Tao, Xiaolong Zhou, Binghui Wu, Hongqiang Wang and Shengyong Chen

Visual tracking technology enables industrial robots interacting with human beings intelligently. However, due to the complexity of the tracking problem, the accuracy of…

Abstract

Purpose

Visual tracking technology enables industrial robots interacting with human beings intelligently. However, due to the complexity of the tracking problem, the accuracy of visual target tracking still has great space for improvement. This paper aims to propose an accurate visual target tracking method based on standard hedging and feature fusion.

Design/methodology/approach

For this study, the authors first learn the discriminative information between targets and similar objects in the histogram of oriented gradients by feature optimization method, and then use standard hedging algorithms to dynamically balance the weights between different feature optimization components. Moreover, they penalize the filter coefficients by incorporating spatial regularization coefficient and extend the Kernelized Correlation Filter for robust tracking. Finally, a model update mechanism to improve the effectiveness of the tracking is proposed.

Findings

Extensive experimental results demonstrate the superior performance of the proposed method comparing to the state-of-the-art tracking methods.

Originality/value

Improvements to existing visual target tracking algorithms are achieved through feature fusion and standard hedging algorithms to further improve the tracking accuracy of robots on targets in reality.

Details

Industrial Robot: the international journal of robotics research and application, vol. 48 no. 5
Type: Research Article
ISSN: 0143-991X

Keywords

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Article
Publication date: 13 July 2012

Weiguo Sheng, Gareth Howells, Michael Fairhurst, Farzin Deravi and Shengyong Chen

Biometric authentication, which requires storage of biometric templates and/or encryption keys, raises a matter of serious concern, since the compromise of templates or…

Abstract

Purpose

Biometric authentication, which requires storage of biometric templates and/or encryption keys, raises a matter of serious concern, since the compromise of templates or keys necessarily compromises the information secured by those keys. To address such concerns, efforts based on dynamic key generation directly from the biometrics have recently emerged. However, previous methods often have quite unacceptable authentication performance and/or small key spaces and therefore are not viable in practice. The purpose of this paper is to propose a novel method which can reliably generate long keys while requires storage of neither biometric templates nor encryption keys.

Design/methodology/approach

This proposition is achieved by devising the use of fingerprint orientation fields for key generation. Additionally, the keys produced are not permanently linked to the orientation fields, hence, allowing them to be replaced in the event of key compromise.

Findings

The evaluation demonstrates that the proposed method for dynamic key generation can offer both good reliability and security in practice, and outperforms other related methods.

Originality/value

In this paper, the authors propose a novel method which can reliably generate long keys while requires storage of neither biometric templates nor encryption keys. This is achieved by devising the use of fingerprint orientation fields for key generation. Additionally, the keys produced are not permanently linked to the orientation fields, hence, allowing them to be replaced in the event of key compromise.

Details

Information Management & Computer Security, vol. 20 no. 3
Type: Research Article
ISSN: 0968-5227

Keywords

Content available
Article
Publication date: 2 March 2012

Abstract

Details

Industrial Robot: An International Journal, vol. 39 no. 2
Type: Research Article
ISSN: 0143-991X

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