Federated learning based nonlinear two-stage framework for full-reference image quality assessment: An application for biometric

Lan, Tianyi; Riaz, Saleem; Zhang, Xuande; Mirza, Alina; Afzal, Farkhanda; Iqbal, Zeshan; Khan, Muhammad Attique; Alhaisoni, Majed; Alqahtani, Abdullah

doi:10.1016/j.imavis.2022.104588

Cited by 4 publications

(2 citation statements)

References 26 publications

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“…However, it is crucial to address security concerns when it comes to UAV technology. UAVs are vulnerable to attacks such as manipulation and interception, which can compromise their integrity and authenticity [10][11][12]. Ensuring security in communication between ground sensors and UAVs is essential, including aspects like non-repudiation, integrity, secrecy, and authentication [13,14].…”

Section: Introductionmentioning

confidence: 99%

UAV-Based Secure Data Communication: Multilevel Authentication Perspective

Aljumah

2024

Sensors

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An Internet of Things (IoT) system for managing and coordinating unmanned aerial vehicles (UAVs) has revolutionized the industrial sector. The largest issue with the design of the Internet of UAVs (IoUAV) is security. Conspicuously, the novel contribution of the proposed work is to develop a layered authentication approach to facilitate safe IoUAV communication. Specifically, four modules, including the pre-deployment module, user registration module, login module, and authentication module, form the basis of security analysis. In the proposed technique, UAVs are added to the IoUAV registry. The next step is the user registration module, where people are registered with the UAV so they may access the information in real time. In the login module, the user connects with the server for data transmission. Finally, in the authentication module, all entities, including users, servers, and UAVs, are authenticated to ensure secure data communication. The proposed method achieves peak performance as compared to the state-of-the-art techniques in terms of statistical parameters of latency (3.255s), throughput (90.15%), and packet loss (8.854%).

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Section: Introductionmentioning

confidence: 99%

UAV-Based Secure Data Communication: Multilevel Authentication Perspective

Aljumah

2024

Sensors

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“…Human motion pattern recognition is one of the key research issues in the field of computer vision [12,14]. Human motion pattern recognition is a process of recognizing motion patterns by recognizing the real-time motion state data of the target human and combining the effective analysis of motion data [18,19].…”

Section: Introductionmentioning

confidence: 99%

Human Motion Pattern Recognition Based on Nano-sensor and Deep Learning

Ji,

Lin

2023

ITC

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A human motion pattern recognition algorithm based on Nano-sensor and deep learning is studied to recognize human motion patterns in real time and with high accuracy. First, human motion data are collected by micro electro mechanical system, and the noise in such data is filtered by smoothing filtering method to obtain high-quality motion data. Second, key time-domain features are extracted from high-quality motion data. Finally, after fusing and processing the key time-domain features, it is input into the deep long and short-term memory (LSTM) neural network to build a deep LSTM human motion pattern recognition model and complete human motion pattern recognition. The results show that the proposed algorithm can realize the recognition of various motion patterns with high accuracy of data acquisition, the average recognition accuracy is 94.8%, the average recall reaches 89.7%, and the F1 score of the algorithm are high, and the recognition time consuming is short, which can realize accurate and efficient human motion pattern recognition and provide guarantee for effective monitoring of the target human motion health.

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Federated learning for biometric recognition: a survey

Guo,

Mu,

Liu

et al. 2024

Artif Intell Rev

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Recently, deep learning (DL) has achieved great success in biometric recognition. The application of DL has also led to a high demand for biometric data. However, as people attach more importance to privacy protection, biometric data have become increasingly difficult to obtain and access, leading to significant limitations in the development and application of DL-based biometric recognition. Federated learning (FL), a distributed learning technique with privacy protection, provides a potential solution to this problem. Several researchers have attempted to integrate FL into biometric recognition. These studies have shown that the introduction of FL not only solves the conflict between privacy and accessibility of biometric data but also improves the accuracy and generalizability of local recognition systems. Therefore, the combination of FL and biometric recognition techniques has become a new research hotspot. In this survey, we comprehensively review the latest advances regarding the application of FL to biometric recognition, biometric presentation attack detection and the related fields to provide new researchers with a quick and systematic overview of this emerging cross-disciplinary field. This paper also summarizes the future opportunities and challenges of this field. To our knowledge, this is the first survey that systematically organizes and analyses federated biometric recognition and related fields to provide suggestions and references for future research.

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Federated learning based nonlinear two-stage framework for full-reference image quality assessment: An application for biometric

Cited by 4 publications

References 26 publications

UAV-Based Secure Data Communication: Multilevel Authentication Perspective

UAV-Based Secure Data Communication: Multilevel Authentication Perspective

Human Motion Pattern Recognition Based on Nano-sensor and Deep Learning

Federated learning for biometric recognition: a survey

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