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2,602 results on '"ZHANG Rui"'

1. A Survey on Biometric Authentication: Toward Secure and Privacy-Preserving Identification

Author

Zhang Rui and Zheng Yan

Subjects
Aliveness detection, biometric authentication, password management, privacy protection, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

In order to overcome the difficulty of password management and improve the usability of authentication systems, biometric authentication has been widely studied and has attracted special attention in both academia and industry. Many biometric authentication systems have been researched and developed, especially for mobile devices. However, the existing biometric authentication systems still have defects. Some biological features have not been deeply investigated. The existing systems could be vulnerable to attacks, such as replay attack and suffer from user privacy intrusion, which seriously hinder their wide acceptance by end users. The literature still lacks a thorough review on the recent advances of biometric authentication for the purpose of secure and privacy-preserving identification. In this paper, we classify and thoroughly review the existing biometric authentication systems by focusing on the security and privacy solutions. We analyze the threats of biometric authentication and propose a number of criteria with regard to secure and privacy-preserving authentication. We further review the existing works of biometric authentication by analyzing their differences and summarizing the advantages and disadvantages of each based on the proposed criteria. In particular, we discuss the problems of aliveness detection and privacy protection in biometric authentication. Based on our survey, we figure out a number of open research issues and further specify a number of significant research directions that are worth special efforts in future research.

Published
2019
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2. Multi-Level Metric Learning Network for Fine-Grained Classification

Author

Jiabao Wang, Yang Li, Zhuang Miao, Xun Zhao, and Zhang Rui

Subjects
Fine-grained recognition, metric learning, multi-level objectives, classification, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

The application of fine-grained image classification can be problematic due to subtle differences between classes. The existing global feature-based methods have worse accuracies than regional feature-based methods, because regional feature-based methods focus on the determination of differentiated features within local regions. To learn more discriminative global features, in this paper, we proposed the use of L2 normalization to tackle a neglected conflict between the widely used metric loss (triplet loss) and classification loss (softmax loss) in global feature-based methods. Furthermore, a multi-level metric learning network (MMLN) is proposed for fine-grained image classification based on global features. In the MMLN, multi-level metric learning objectives and classification objectives are present at multiple high-level layers. The multi-level metric learning objectives work together to supervise the network in order to learn highly discriminative features. In addition, a new probability aggregation strategy (PAS) is proposed to produce a fused prediction by combining the multi-level predictive probabilities. Experiments were conducted on three standard fine-grained classification datasets (CUB-200-2011, Stanford Cars, and FGVC-Aircraft). Results demonstrated that our MMLN achieved accuracies of 88.0%, 94.6% and 92.4% respectively and outperformed state-of-the-art methods, substantially improving fine-grained classification tasks. Besides, gradient-weighted class activation mapping (Grad-CAM) shows that the MMLN is able to pay more attention to the discriminative local regions due to the application of multi-level metric learning.

Published
2019
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25. An Objective Space Constraint-Based Evolutionary Method for High-Dimensional Feature Selection [Research Frontier].

Author

Cheng, Fan, Zhang, Rui, Huang, Zhengfeng, Qiu, Jianfeng, Xia, Mingming, and Zhang, Lei

Abstract

Evolutionary algorithms (EAs) have shown their competitiveness in solving the problem of feature selection. However, limited by their encoding scheme, most of them face the challenge of "curse of dimensionality". To address the issue, in this paper, an objective space constraint-based evolutionary algorithm, named OSC-EA, is proposed for high-dimensional feature selection (HDFS). Although the decision space of EAs for HDFS is very huge, its objective space is the same as that of the low-dimensional feature selection. Based on this fact, in the proposed OSC-EA, the HDFS is firstly modeled as a constrained problem, where a constraint of the objective space is introduced and used to partition the whole objective space into the "feasible region" and the "infeasible region". To handle the constrained problem, a two-stage $\varepsilon$ɛ constraint-based evolutionary scheme is designed. In the first stage, the value of $\varepsilon$ɛ is set to be very small, which ensures that the search concentrates on the "feasible region", and the latent high-quality feature subsets can be found quickly. Then, in the second stage, the value of $\varepsilon$ɛ increases gradually, so that more solutions in the "infeasible region" are considered. Until the end of the scheme, $\varepsilon \rightarrow \infty$ɛ→∞; all the solutions in the objective space are considered. By using the search in the second stage, the quality of the obtained feature subsets is further improved. The empirical results on different high-dimensional datasets demonstrate the effectiveness and efficiency of the proposed OSC-EA. [ABSTRACT FROM AUTHOR]

Published
2024
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45. Investigation of Random Telegraph Noise in Advanced Silicon-On-Insulator N-FETs: The Impact of Back Bias, Strain, and Hot Carrier Stress

Author

Li, Xinze, primary, Wu, Yuxuan, additional, Teng, Qiao, additional, Sun, Ying, additional, Gong, Xiao, additional, Besnard, Guillaume, additional, Maleville, Christophe, additional, Weber, Olivier, additional, Zhang, Rui, additional, Chen, Bing, additional, Gao, Dawei, additional, and Cheng, Ran, additional

Published
2023
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