## 5 Conclusions  
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. In this research, several statistical and kinematic features are extracted for classifying the users. However, not all the features are vital for the authentication of a user. Moreover, sometimes, extra features may degrade the performance of classifiers. Therefore, a hybrid optimal feature selection algorithm based on the filter and wrapper approach has been introduced to get the best optimal features from original high dimension feature vector. The selected features are then used for training and authenticating the writes using SVM, LR, and RF to identify based on person's handwriting. To validate the proposed model, different handwritings from different users are collected using digital pen tablet. Ten specific keywords were written by the users iteratively. The proposed system shows 90% and 92% average classification results with feature selection techniques which are around 7% and 6% improved and higher performance than without feature selection technique using LR and RF respectively. The proposed system also shows 97% average classification results with feature selection technique which is around 7% improved and  
Figure 19: Class-wise accuracy (\(\%\)) comparison of the proposed model with state-of-the-art image-based system. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Ref \#** & **Classifier** & **Feature** & **Result/Accuracy** & **Input Data Type** & **Comments** \\ \hline
[5] & RF, KNN & Keystrokes Dynamic &
\begin{tabular}{c} Achieved an \\ ER of 2.9\% \\ \end{tabular} & Image-based & Considered only 3 typing positions \\ \hline
[11] & KNN, SVM & Hand Radiographs & 97\% & Image-based &
\begin{tabular}{c} Suffers from high computational \\ Cost \\ \end{tabular} \\ \hline
[28] & \
\begin{tabular}{c} Minimum \\ Distance, \\ Bayes \\ \end{tabular} & Dynamic Features & 95\% & Pen-tablet data &
\begin{tabular}{c} Focused only on the small scale \\ writing samples \\ \end{tabular} \\ \hline
[30] & \
\begin{tabular}{c} Hamming \\ Nearest \\ neighbor \\ \end{tabular} & Histogram & 91.17\% & Image-based &
\begin{tabular}{c} Writer’s information lost due to \\ stretching the images \\ \end{tabular} \\ \hline
[32] & AlexNet & Implicit and explicit information & 92\% & Image-based &
\begin{tabular}{c} Resizing handwriting patches loss \\ write’s intrinsic information \\ \end{tabular} \\ \hline
[34] & ResNet & \begin{tabular}{c} Handwriting thickness descriptor \\ (HTD) \\ \end{tabular} & 97\% & Image-based & \begin{tabular}{c} High Computational cost, \\ Multimodal descriptions needed \\ \end{tabular} \\ \hline Proposed Model & \
\begin{tabular}{c} SVM, LR, \\ RF \\ \end{tabular} & Optimal Features (kinematic and  statistics) & 98\% & Pen-tablet sensor data (numerical value) &
\begin{tabular}{c} Pen-tablet data are more robust in \\ terms of noise effect compare to \\ image-based, low computational \\ cost \\ \end{tabular} \\ \hline \end{tabular}
\end{table} 
Table 8: Comparison of Proposed Scheme with State of Art. higher performance than the no feature selection phase using SVM. Among the 3 machine learning algorithms, the SVM performs outstanding compared to the rest. The proposed system can successfully classify the given handwriting dataset. Thus, the system can be applied in some real applications like signature verification in the banking sector, proving the writer in forensic analysis, analysis of mental states of the writer, and so on. As the proposed system uses more genuine and robust sensor data for analyzing the handwriting, it can accurately predict the writer with limited computational resources and hardware costs which can be a good application in the case of small mobile devices. Conceptualization, N.B. and M.R.I. ; Data curation, N.B., M.A.H.A. and S.R. ; Formal analysis, N.B., M.A.H.A. and S.R. ; Funding acquisition, N.B., M.E.I. ; Investigation, M.R.I. and M.E.I. ; Methodology, N.B., M.A.H.A., J.S.