user identification
Recently Published Documents


TOTAL DOCUMENTS

581
(FIVE YEARS 189)

H-INDEX

27
(FIVE YEARS 7)

2022 ◽  
Vol 11 (1) ◽  
pp. 1-50
Author(s):  
Bahar Irfan ◽  
Michael Garcia Ortiz ◽  
Natalia Lyubova ◽  
Tony Belpaeme

User identification is an essential step in creating a personalised long-term interaction with robots. This requires learning the users continuously and incrementally, possibly starting from a state without any known user. In this article, we describe a multi-modal incremental Bayesian network with online learning, which is the first method that can be applied in such scenarios. Face recognition is used as the primary biometric, and it is combined with ancillary information, such as gender, age, height, and time of interaction to improve the recognition. The Multi-modal Long-term User Recognition Dataset is generated to simulate various human-robot interaction (HRI) scenarios and evaluate our approach in comparison to face recognition, soft biometrics, and a state-of-the-art open world recognition method (Extreme Value Machine). The results show that the proposed methods significantly outperform the baselines, with an increase in the identification rate up to 47.9% in open-set and closed-set scenarios, and a significant decrease in long-term recognition performance loss. The proposed models generalise well to new users, provide stability, improve over time, and decrease the bias of face recognition. The models were applied in HRI studies for user recognition, personalised rehabilitation, and customer-oriented service, which showed that they are suitable for long-term HRI in the real world.


Symmetry ◽  
2022 ◽  
Vol 14 (1) ◽  
pp. 110
Author(s):  
Yating Qu ◽  
Ling Xing ◽  
Huahong Ma ◽  
Honghai Wu ◽  
Kun Zhang ◽  
...  

Identifying offline entities corresponding to multiple virtual accounts of users across social networks is crucial for the development of related fields, such as user recommendation system, network security, and user behavior pattern analysis. The data generated by users on multiple social networks has similarities. Thus, the concept of symmetry can be used to analyze user-generated information for user identification. In this paper, we propose a friendship networks-based user identification across social networks algorithm (FNUI), which performs the similarity of multi-hop neighbor nodes of a user to characterize the information redundancy in the friend networks fully. Subsequently, a gradient descent algorithm is used to optimize the contribution of the user’s multi-hop nodes in the user identification process. Ultimately, user identification is achieved in conjunction with the Gale–Shapley matching algorithm. Experimental results show that compared with baselines, such as friend relationship-based user identification (FRUI) and friendship learning-based user identification (FBI): (1) The contribution of single-hop neighbor nodes in the user identification process is higher than other multi-hop neighbor nodes; (2) The redundancy of information contained in multi-hop neighbor nodes has a more significant impact on user identification; (3) The precision rate, recall rate, comprehensive evaluation index (F1), and area under curve (AUC) of user identification have been improved.


Author(s):  
Dian Ding ◽  
Lanqing Yang ◽  
Yi-Chao Chen ◽  
Guangtao Xue

The convenience of laptops brings with it the risk of information leakage, and conventional security systems based on the password or the explicit biometric do little to alleviate this problem. Biometric identification based on anatomical features provides far stronger security; however, a lack of suitable sensors on laptops limits the applicability of this technology. In this paper, we developed a behavior-irrelevant user identification system applicable to laptops with a metal casing. The proposed scheme, referred to as LeakPrint, is based on leakage current, wherein the system uses an earphone to capture current leaking through the body and then transmits the corresponding signal to a server for identification. The user identification is achieved via denoising, dimension reduction, and feature extraction. Compared to other biometric identification methods, the proposed system is less dependent on external hardware and more robust to environmental noise. The experiments in real-world environments demonstrated that LeakPrint can verify user identity with high accuracy (93.6%), while providing effective defense against replay attacks (96.5%) and mimicry attacks (90.9%).


Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8129
Author(s):  
Do-Yun Kim ◽  
Seung-Hyeon Lee ◽  
Gu-Min Jeong

In this study, we propose a long short-term memory (LSTM)-based user identification method using accelerometer data from smart shoes. In general, for the user identification with human walking data, we require a pre-processing stage in order to divide human walking data into individual steps. Next, user identification can be made with divided step data. In these approaches, when there exist partial data that cannot complete a single step, it is difficult to apply those data to the classification. Considering these facts, in this study, we present a stack LSTM-based user identification method for smart-shoes data. Rather than using a complicated analysis method, we designed an LSTM network for user identification with accelerometer data of smart shoes. In order to learn partial data, the LSTM network was trained using walking data with random sizes and random locations. Then, the identification can be made without any additional analysis such as step division. In the experiments, user walking data with 10 m were used. The experimental results show that the average recognition rate was about 93.41%, 97.19%, and 98.26% by using walking data of 2.6, 3.9, and 5.2 s, respectively. With the experimental results, we show that the proposed method can classify users effectively.


Author(s):  
Tao Zhang ◽  
Xinzhou Cheng ◽  
Jie Gao ◽  
Lexi Xu ◽  
Chen Cheng ◽  
...  

Author(s):  
Dr. C. K. Gomathy

Abstract: The Twitter has fleetly come an online source for acquiring real- time his/ her information about druggies. Twitter is an Online Social Network (OSN) where druggies can partake anything and everything, similar as news, opinions, and indeed their moods. Several arguments can be held over different motifs, similar as politics, Perticular affairs, and important events. When a stoner tweets commodity, it's incontinently conveyed to her followers, allowing them to unfold the entered information at a much broader position. With the elaboration of OSNs, the need to study and dissect druggies' actions in online social platforms has intensity Spammers can be linked grounded on (i) fake content, (ii) URL grounded spam discovery, (iii) spam in trending motifs, and (iv) fake stoner identification. And with the help of machine literacy algorithms we're going to identify the fake stoner and spammer in twitter. Keywords: Online Social Network, Spammers Identification, Fake User Identification.


Author(s):  
Aakshi Mittal ◽  
Mohit Dua

AbstractDetection of spoof is essential for improving the performance of current scenario of Automatic Speaker Verification (ASV) systems. Empowerment to both frontend and backend parts can build the robust ASV systems. First, this paper discuses performance comparison of static and static–dynamic Constant Q Cepstral Coefficients (CQCC) frontend features by using Long Short Term Memory (LSTM) with Time Distributed Wrappers model at the backend. Second, it performs comparative analysis of ASV systems built using three deep learning models LSTM with Time Distributed Wrappers, LSTM and Convolutional Neural Network at backend and using static–dynamic CQCC features at frontend. Third, it discusses implementation of two spoof detection systems for ASV by using same static–dynamic CQCC features at frontend and different combination of deep learning models at backend. Out of these two, the first one is a voting protocol based two-level spoof detection system that uses CNN, LSTM model at first level and LSTM with Time Distributed Wrappers model at second level. The second one is a two-level spoof detection system with user identification and verification protocol, which uses LSTM model for user identification at first level and LSTM with Time Distributed Wrappers for verification at the second level. For implementing the proposed work, a variation in ASVspoof 2019 dataset has been used to introduce all types of spoofing attacks such as Speech Synthesis (SS), Voice Conversion (VC) and replay in single set of dataset. The results show that, at frontend, static–dynamic CQCC feature outperform static CQCC features and at the backend, hybrid combination of deep learning models increases accuracy of spoof detection systems.


Sign in / Sign up

Export Citation Format

Share Document