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User Authentication Based on Handwriting Analysis of Pen-Tablet Sensor Data Using Optimal Feature Selection Model

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Department of Computer Science and Engineering, University of Asia Pacific, Dhaka 1216, Bangladesh
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School of Computer Science and Engineering, University of Aizu, Aizu-Wakamatsu, Fukushima 965-8580, Japan
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Department of Computer Science and Engineering, Jahangirnagar University, Dhaka 1342, Bangladesh
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Authors to whom correspondence should be addressed.
This paper is an extended version of “User Authentication Through Pen Tablet Data Using Imputation and Flatten Function” published in the Proceedings of 2020 3rd IEEE International Conference on Knowledge Innovation and Invention (ICKII), Kaohsiung, Taiwan, 21–23 August 2020.
Academic Editor: Efstathios Stamatatos
Future Internet 2021, 13(9), 231; https://doi.org/10.3390/fi13090231
Received: 1 August 2021 / Revised: 27 August 2021 / Accepted: 29 August 2021 / Published: 6 September 2021
(This article belongs to the Section Big Data and Augmented Intelligence)
Handwriting analysis is playing an important role in user authentication or online writer identification for more than a decade. It has a significant role in different applications such as e-security, signature biometrics, e-health, gesture analysis, diagnosis system of Parkinson’s disease, Attention-deficit/hyperactivity disorders, analysis of vulnerable people (stressed, elderly, or drugged), prediction of gender, handedness and so on. Classical authentication systems are image-based, text-dependent, and password or fingerprint-based where the former one has the risk of information leakage. Alternatively, image processing and pattern-analysis-based systems are vulnerable to camera attributes, camera frames, light effect, and the quality of the image or pattern. Thus, in this paper, we concentrate on real-time and context-free handwriting data analysis for robust user authentication systems using digital pen-tablet sensor data. Most of the state-of-the-art authentication models show suboptimal performance for improper features. This research proposed a robust and efficient user identification system using an optimal feature selection technique based on the features from the sensor’s signal of pen and tablet devices. The proposed system includes more genuine and accurate numerical data which are used for features extraction model based on both the kinematic and statistical features of individual handwritings. Sensor data of digital pen-tablet devices generate high dimensional feature vectors for user identification. However, all the features do not play equal contribution to identify a user. Hence, to find out the optimal features, we utilized a hybrid feature selection model. Extracted features are then fed to the popular machine learning (ML) algorithms to generate a nonlinear classifier through training and testing phases. The experimental result analysis shows that the proposed model achieves more accurate and satisfactory results which ensure the practicality of our system for user identification with low computational cost. View Full-Text
Keywords: user authentication; handwriting analysis; optimal feature; feature selection; machine learning; SVM; F-1 score; sensor data; SFFS user authentication; handwriting analysis; optimal feature; feature selection; machine learning; SVM; F-1 score; sensor data; SFFS
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MDPI and ACS Style

Begum, N.; Akash, M.A.H.; Rahman, S.; Shin, J.; Islam, M.R.; Islam, M.E. User Authentication Based on Handwriting Analysis of Pen-Tablet Sensor Data Using Optimal Feature Selection Model. Future Internet 2021, 13, 231. https://doi.org/10.3390/fi13090231

AMA Style

Begum N, Akash MAH, Rahman S, Shin J, Islam MR, Islam ME. User Authentication Based on Handwriting Analysis of Pen-Tablet Sensor Data Using Optimal Feature Selection Model. Future Internet. 2021; 13(9):231. https://doi.org/10.3390/fi13090231

Chicago/Turabian Style

Begum, Nasima, Md A.H. Akash, Sayma Rahman, Jungpil Shin, Md R. Islam, and Md E. Islam. 2021. "User Authentication Based on Handwriting Analysis of Pen-Tablet Sensor Data Using Optimal Feature Selection Model" Future Internet 13, no. 9: 231. https://doi.org/10.3390/fi13090231

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