Abstract
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.
Highlights
In the modern age of information technology, user authentication is an important process for information security and IoT-based systems
6 presents theabout best accuracy of shows selected best optimal wrapper to prove the stability of our system, we implemented our model with two additional approach using support vector machine, logistic regression, and random forest
This paper proposed a user authentication system by analyzing digital pen-tablet sensor data by optimal feature selection model which can successfully identify the writer
Summary
In the modern age of information technology, user authentication is an important process for information security and IoT-based systems. An image-based model does not work with the genuine and exact numerical values of an individual’s handwriting data. In this research, we concentrate on collecting the more genuine numerical values of the user’s handwriting directly from the sensors signal of digital pen-tablet devices which motivates this work. The proposed model is constructed based on the user’s pen tablet handwriting data analysis which automatically stores the numeric values of the user’s handwriting attributes from the pen-tablet sensor signals. Since the data values are directly extracted from the sensor signal of digital pen and tablet devices, it ensures the robustness of the identification of handwriting data. In the proposed user authentication model, firstly, six completely separate parameters of handwritten data are collected from the sensor signals of digital pen-tablet devices. A quantitative analysis of pen-tablet sensor data using kinematic and statistical features extraction model. Each section includes the necessary diagrams, tables, and graphical representations for an easy and clear understanding of this research work
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
