## 3 Results and Discussion
### Qualitative Analysis of the Proposed Technique  
#### 3.2.1 Qualitative Comparison of the Proposed Simple Model with the FACS System  
Today, the facial action coding system (FACS), developed by Professor [PERSON] [10], is the generally accepted methodology for describing changes in emotional states on the human face. FACS serves as a human-observer recognition system for detecting subtle changes in facial features. It presents the human face in the form of 98 fully controlled models, the so-called action units (AUs), each responsible for specific facial expressions. Currently, the FACS coding system's AUs are considered the benchmark for identifying different emotional facial expressions. Work [10] highlights and describes in detail four universal human emotional states, i.e., Anger, Fear, Joy, and Sadness. These emotions are manifested by different facial expressions and described by a unique set of AUs.  
The FACS system characterizes the degree of expressiveness of changes in the condition of the muscles with five levels: A--minimal, B--insignificant, C--obvious, D--extreme, and E--maximum. In the proposed simple model, the change in an emotional state is determined according to a predetermined standard against the approach with active units in [PERSON]'s work. That is, the same features are defined for different emotions but with different meanings of facial expressions.  
We compared the proposed simple model of facial expressions of emotional states (1) based on the qualitative characteristics of facial features (Table 4) with the FACS human facial expression classification system. The following emotions were taken for comparison: Anger, Fear, Joy, Neutral, and Sadness. A qualitative comparison of the proposed model with the FACS system is presented in Table 6.  
It is worth noting from Table 6 that the proposed model does not track the lowering of the jaw. FACS examines the sequence of different facial expressions. Sometimes these manifestations are opposite, which indicates a redundancy of features and leads to manifestations of ambiguity, as in the case of the Fear condition (raising the eyebrows, then lowering them). On the other hand, the proposed model tracks one pattern of facial expressions (seven qualitative characteristics of facial areas), which most characteristically allows for the separation of emotional states.  
A comparative analysis of the FACS system and the proposed model based on identifying and selecting influential features allows for identifying changes in the emotional state of facial expressions without reducing quality performance indicators. Using the model allows the determination of the set of necessary emotional states and forms the necessary set of characteristic features that create an emotional manifestation and are characterized by the appropriate parameters for separating emotions by facial expressions.  
At the same time, the unification of the number of qualitative structural features of facial expressions has shown its effectiveness because significant features are determined, which collectively create conditions for a good separation of these groups and, therefore, separation of emotional states.