## 3 Results and Discussion
### Quantitative Comparison of the Proposed Technique with the Analogs  
Experimental testing of the proposed technique was performed using two reference datasets. To ensure the purity of the experiment, small balanced datasets were selected from the reference datasets. The performance of the technique was evaluated by the statistical indicators of Accuracy, Precision, Recall, and F1-Score. Four more ML and DL models were used for the comparison with the proposed technique. 1. Model_1: EmoFan [48] is a model proposed in a research paper. It classifies 20 different emotional categories using a combination of convolutional neural networks (CNNs) and closed recurrent units. 2. Model_2: OpenFace [10; 49] is an open-source package of tools and models for ML/DL developed at Carnegie Mellon University that enables users to recognize facial expressions and categorize a person's emotional state. 3. Model_3: DeepFace [50] is a software framework that contains implementations of various ML/DL models and is designed to process photo and video materials with human faces, in particular, to identify emotional states by facial expressions. 4. Model_4: LHC-Net [51] is a new model based on CNNs and an integrated module of self-awareness designed to identify emotional states by facial expressions. 5. Model_5: The proposed technique (see Figure 9) with the constructed classifier (13) for identifying changes in a person's emotional state based on facial expressions for photo and video surveillance. The results of the experimental tests on five models (Model_1, Model_2, Model_3, Model_4, and Model_5), which were evaluated by the ability to classify five emotions (Anger, Fear, Joy, Neutral, and Sadness) according to the selected statistical indicators using the FER+small reference set, are presented in the form of discrepancy matrices in Figure 12.  
Figure 12: _Cont._  
A visual comparison of the models by statistical indicators for each emotional state on the FER+small dataset is shown in Figure 13.  
The results of experimental testing of the five models (Model_1, Model_2, Model_3, Model_4, and Model_5), which were evaluated by their ability to classify five emotions (Anger, Fear, Joy, Neutral, and Sadness) according to the selected statistical indicators from the ADFES-small reference set, are presented in the form of discrepancy matrices in Figure 14.  
A visual comparison of the models by statistical indicators for each emotional state on the ADFES-small dataset is shown in Figure 15.  
Figure 12: Confusion matrices obtained by (**a**) Model_1, (**b**) Model_2, (**c**) Model_3, (**d**) Model_4, and (**e**) Model_5 based on the FER+small dataset. Figure 13: Comparison of models by Precision, Recall, F1-score, and Accuracy for emotions: (**a**) Anger, (**b**) Fear, (**c**) Joy, (**d**) Neutral, and (**e**) Sadness on the FER+small dataset. Figure 14: Confusion matrices obtained by (**a**) Model_1, (**b**) Model_2, (**c**) Model_3, (**d**) Model_4, and (**e**) Model_5 on the ADFES-small dataset. Figure 15: Comparison of models by Precision, Recall, F1-score, and Accuracy for emotions: (**a**) Anger, (**b**) Fear, (**c**) Joy, (**d**) Neutral, and (**e**) Sadness on the ADFES-small dataset. It can be observed from Figures 12-15 that Model_5 demonstrates the best results, while maintaining a high level of performance for all statistical indicators and demonstrating overall accuracy for all emotional states. The high Precision and Recall values indicate that Model_5 can obtain reliable results in the task of classifying true positive cases and limit the number of false positives and false negatives. In particular, Model_5 perfectly recognizes the emotion Fear with Precision--80.15% and 78.26%, Recall--81.64% and 81.82%, and F1-Score--80.89% and 80.00%, which demonstrates the ability of Model_5 to correctly identify this emotional state by facial expressions with a high level of reliability. The differences in the values of the indicators between Model_5 and its analogs are also revealed.