## 1 Introduction  
Video surveillance systems are commonly used in various settings to ensure safety and security, particularly in places with large groups of people, such as public spaces, transportation systems, and workplaces. However, these systems face several challenges [1, 2, 3], including the need to monitor and respond to potential threats in real time effectively [4]. One of the main problems with traditional video surveillance systems is that they rely on human operators to monitor [5] and interpret the behavior of individuals in real time [6]. Such an outcome can be challenging, particularly in crowded environments, where it can be difficult to distinguish between normal and suspicious behavior [7]. Additionally, operators may suffer from fatigue or inattention [8], leading to missed or delayed responses [9]. Emotion recognition technologies can help address the above-mentioned challenges by automatically analyzing individuals' facial expressions [10] and body language in real-time to identify potential emotional states that may indicate a threat or suspicious behavior. Such technologies employ machine learning (ML) algorithms [11, 12, 13] to detect changes in facial expressions, such as frowns, raised eyebrows, or narrowed eyes, which may indicate negative emotions like Anger, Fear, or Sadness [14; 15]. By incorporating emotion recognition technology into video surveillance systems [16], security personnel can receive alerts or notifications when potentially suspicious behavior is detected. This can enable a more targeted response and improve the effectiveness of security measures. For example, security personnel could be alerted to potential threats before they escalate [17; 18], allowing for a more rapid and effective response. Technologies with an emotion recognition approach can also help improve public safety by identifying individuals who may require additional support or intervention, such as those experiencing distress [19] or mental health issues [20]. This could be instrumental in high-stress environments like airports, train stations, or crowded public events, where individuals may be more prone to emotional distress. Previously, some studies have investigated the use of emotion recognition techniques based on ML [21; 22; 23] in video surveillance systems for large groups of people. For instance, a study [24] explored using facial expression recognition technology to detect aggression and violence in crowded public spaces. The system used a traditional ML algorithm to analyze facial expressions and identify potential threats, such as angry or aggressive behavior. The system was tested in a simulated environment and achieved an accuracy rate of over 90%. In another study [25], researchers developed a real-time emotion recognition system that could analyze the emotional states of individuals in a crowded environment. The system used a combination of facial expression recognition and body language analysis to identify potential threats or emotional distress. It was tested in a shopping mall and achieved an accuracy rate of over 80%. Another work [26] explored the use of emotion recognition technology in video surveillance systems for workplace safety. The system used facial expression recognition and physiological signal analysis to detect potential safety hazards and alert workers in real time. The system was tested in a manufacturing facility and achieved an accuracy rate of over 90%. On the other hand, recent research has explored the potential of implementing emotion recognition technology based on deep learning (DL) [27; 28; 29] in video surveillance systems for large groups of people. These studies have shown promising results in detecting emotions such as Anger, Fear, Joy, and Sadness from facial expressions. For example, a study published in [30] explored using DL-based emotion recognition algorithms to detect aggression and violence in crowded public spaces.