## 1 Introduction
The system was able to detect aggression with an accuracy of over 90% by analyzing facial expressions obtained by unmanned aerial vehicles. Similarly, another work [31] demonstrated the potential of DL-based emotion recognition in detecting depression and anxiety among college students through facial expressions. The system achieved an accuracy rate of over 80% in identifying depression and anxiety from facial expressions. In the above-mentioned regard, the use of high-precision facial landmark extractors is increasingly becoming a central aspect of geometrical feature extraction in emotion recognition. For instance, [PERSON] and [PERSON] [32] proposed a method to achieve exact face alignment in just one millisecond. This approach uses an ensemble of regression trees to predict facial landmark positions, offering a significant advantage in speed and precision. Such rapid, precise facial alignment can be instrumental in real-time emotion recognition, as it facilitates rapid extraction of geometrical features that directly contribute to recognizing the emotional state. [PERSON] et al. further built upon this approach in [33]. Their research underscores the utility of recurrent neural networks (RNNs) for emotion recognition. By effectively capturing temporal dynamics, RNNs can model long-term emotional behavior based on a sequence of facial landmarks. This approach enables more accurate and nuanced emotion recognition by accounting for temporal variations in facial expressions, enhancing the understanding of emotional states over time. Lastly, in [34], the authors presented a novel approach to mitigate the influence of identity-related attributes on emotion recognition. Their identity-invariant facial landmark frontalization technique ensures that the extracted facial landmarks are invariant to personal identities,focusing instead on emotion-expressive facial movements. This is particularly useful in emotion recognition, as it helps to avoid potential biases and improves the system's generalizability. The analyzed studies underscore the importance of high-precision facial landmark extractors in emotion recognition. Adopting efficient face alignment techniques, long-term behavior modeling, and identity-invariant facial landmark extraction makes achieving more accurate and robust emotion recognition systems possible. Nevertheless, despite the promising results mentioned above, such studies and other related research often lack interpretability and transparency in their findings [35, 36]. It can be problematic to explain how the modern DL models arrived at their conclusions, which raises concerns around potential biases and ethical considerations raised by a novel concept of FACTS (Fairness, Accountability, Confidentiality, Transparency, and Safety) [37] in AI. Furthermore, DL models require a large amount of data to train, making it challenging to ensure the models accurately capture the diversity of emotions and expressions [38]. This can lead to potential biases and limitations [39] in the ability of these models to generalize to different situations in public spaces. Therefore, there is a need for continued research into the designing of interpretable and more transparent emotion recognition models that can effectively detect emotions in safety video surveillance systems. Overall, the goal of this study is to improve the accuracy of identifying changes in the emotional state of a person through facial expressions by designing a novel technique to detect abnormal behavior of a group of people in a crowd via their facial expressions in systems that meet security requirements. The conducted analysis identified and highlighted the following visual facial features that may indicate abnormal behavior of a group of people, which should be detected as soon as possible from the video stream of the video surveillance system:  
* Rapid change of emotional state. In a normal conversation or interaction, people usually do not move quickly from one emotion to another.