## 3 Experimental Results and Discussion
### Comparison of Our Proposed Method with the Existing Method  
The comparison of the classification accuracy of our proposed method with the existing method in the literature is presented in Table 12. [PERSON] et al. (2017) [4] applied different ML algorithms such as multilayer perception (MLP), deep convolutional neural network (DCNN), decision tree (DT), RF, and SVM for the classification of adult and teenager age groups based on sentences. They collected 7000 sentences for the classification of age groups (teenager vs. adult). They showed that DCNN had a better performance and obtained 95.0% precision. [PERSON] et al. (2021) [31] proposed a novel method for the classification of human age. They extracted features using interior angle formulation, anthropometric model, carino-facial development, wrinkle detection, and heat maps. The best combination of feature sets was selected using SFS. They adopted CNN to classify human age and achieved 94.6% classification accuracy. [PERSON] and [PERSON] (2018) [32] proposed a deep learning algorithm for the classification of people based on their age. They divided the people into 12 classes using age groups and collected 18,000 images. They took 10% of the images for testing and the rest of the images for training. They showed that the DL model can correctly classify people into different groups of age and achieved 78.5% classification accuracy. \begin{table}
\begin{tabular}{l l l l} \hline
**Line Types** & **CF** & **RF** & **SVM** \\ \hline Zigzag trace & 18 & 79.5 & 71.4 \\ Zigzag predict & 2 & 73.4 & 69.3 \\ PL trace & 7 & 83.6 & 79.5 \\ PL predict & 9 & 89.8 & 79.5 \\ All patterns & 25 & 84.1 & 85.1 \\ \hline \multicolumn{3}{l}{CF: Common features.} \\ \end{tabular}
\end{table} 
Table 10: Classification accuracy (in %) of RF and SVM for common features of handwritten pattern dataset. \begin{table}
\begin{tabular}{l l l l l l l l l} \hline \multirow{2}{*}{**Line Types**} & \multirow{2}{*}{**CF**} & \multicolumn{3}{c}{**RF**} & \multicolumn{3}{c}{**SVM**} \\ \cline{3-8}  & & **Rec** & **Prec** & **f1-Score** & **AUC** & **Rec** & **Prec** & **f1-Score** & **AUC** \\ \hline Zigzag trace & 18 & 80.6 & 86.2 & 83.3 & 0.870 & 67.7 & 84.0 & 75.0 & 0.820 \\ Zigzag predict & 2 & 83.8 & 76.4 & 80.0 & 0.784 & 77.4 & 75.0 & 76.1 & 0.732 \\ PL trace & 7 & 83.8 & 89.6 & 86.6 & 0.926 & 74.1 & 92.0 & 82.1 & 0.872 \\ PL predict & 3 & 87.0 & 93.1 & 90.0 & 0.903 & 83.8 & 83.8 & 83.8 & 0.811 \\ All patterns & 23 & 85.1 & 84.3 & 84.7 & 0.923 & 83.1 & 87.5 & 85.2 & 0.919 \\ \hline \end{tabular}
\end{table} 
Table 11: Performance scores of RF and SVM for common features of handwritten pattern dataset. [PERSON] (2019) [33] proposed a novel Poincare feature set to classify age and gender based on ECG. They collected ECG data from 79 respondents. Among them, 37 were males aged 31.24 years and 42 were females aged 25.8 years. They applied SVM for the classification of age and gender and obtained the highest classification accuracy of 94.6%. [PERSON] et al. (2020) [34] investigated a novel biometric method for the classification of human age. For classification, RF, SVM, linear regression (LR), ridge regression (RR), polynomial regression (PR), and ANN were used. They collected a total of 837 subjects aged 6-60 years to evaluate the proposed biometric system. They showed that RF produced the highest classification accuracy of 92.0%. Voice is also used for user authentication and identification. Voiceprints were used in various forensic approaches to classify age, gender, and language. [PERSON] et al. (2015) [35] conducted a study for the classification of adult, child, and senior using face images dataset. They extracted features using HOG, local binary pattern, and active appearance model. They adopted k-NN, SVM, and GB algorithms for the classification of adult, child, and senior and achieved 82.0% classification accuracy. [PERSON] (2012) [36] applied PCA for the classification of age using face image and produced the highest classification accuracy of 92.5%.