## 7 Complex Discussion--Evaluation of Results from Experiments  
We tested the ML.NET model by selecting other different predefined pre-trained ResNet models. The model was tested with the integration of ResNet50, ResNet101 and ResNet152. Epochs were tested with values of 50, 100 and 200 and learning speed values of 0.01 and 0.001. The number of samples determined between one forward and reverse learning cycle was tested with values of 10, 15 and 30. The EmguCV model demonstrated classification capability with an average success rate of 81.95%, and the model created in ML.NET, based on the pre-trained ResNet50 model, demonstrated classification capability with an average of up to 91.15% accuracy. The success rate of both models exceeded 80%, and therefore, we can consider them successful. We created the third model in the Tensorflow.Net software library. The aim of this model was to create a model of a multilayer neural network that would be able to classify the face of a person in an image based on the data of vector distances between individual facial features. Based on the selected network settings and input data type, this model demonstrated the ability to successfully classify with only 13.31% on average. Therefore, we consider this model not applicable for face classification based on vector values of distances of individual areas of interest. However, these data could be suitable as supplementary data to other neural network models, which would serve as extra data describing individuals. The EmguCV model implements the classification directly from the extracted pixel values of the image with a resolution of 28 \(\times\) 28 pixels. The ML.NET model converts the input image with a resolution of 224 \(\times\) 224 pixels by a series of convolutional and associative layers, and classification is then performed based on their outputs. For this reason, the time required to train the model created in EmguCV is significantly less. The average training time for this model is 117.1 s, while the average training time for the ML.NET model is 1441.8 s, as the input dimension of the image is much larger and contains many layers. ML.NET has a higher success rate in classifying faces by 9.2%, and the time required for classification is longer, though. This makes the EmguCV model more suitable for real-time classification. The summary table shows the significant differences between the individual models, both in terms of the time needed for training and the overall accuracy of the classification (Table 1). Our experiments successfully demonstrated that the multilayer neural network model in the EmguCV framework is capable of reliable classification of the faces on which it was trained. EmguCV is a popular library used mainly for face detection and extraction  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**EmguCV**} & \multicolumn{2}{c}{**Tensorflow.Net**} & \multicolumn{2}{c}{**MLNet**} \\ \hline
**No. ** & **Accuracy (\%)** & **Time (s)** & **Accuracy (\%)** & **Time (s)** & **Accuracy (\%)** & **Time (s)** \\ \hline
1 & 84.55 & 117 & 14.29 & 14 & 92.53 & 1428 \\
2 & 83.19 & 119 & 15.12 & 15 & 91.75 & 1393 \\
3 & 84.87 & 120 & 14.29 & 15 & 92.14 & 1453 \\
4 & 81.51 & 115 & 11.9 & 13 & 91.84 & 1427 \\
5 & 80.67 & 11 by pre-trained models with the possibility of their modification. Many scientific articles and publications [38], ref. [43] use the EmguCV library for only two phases: detection and extraction. Neural network models are often created in other frameworks, which provide more algorithms and also the possibility of more detailed modification of models when creating neural networks, but the training is implemented on a smaller amount of data. The EmguCV model we designed was trained on a dataset containing 1454 face images belonging to 43 different people. Although our dataset contains a larger number of classification classes compared to them, the model is still able to correctly classify faces with approximately the same accuracy as the described models [38; 43].