Affective computing is becoming more and more popular, and the need to find a user-friendly and reliable method of estimating people’s emotions, in their everyday life, is growing. Traditional methods have reached their limits, and this thesis presents a new system of emotion recognition, though physiological signals. With a user-friendly, wearable device, the system can be deployed in a number of fields. A model for our emotion classification is presented and includes the following emotions: cheerfulness, sadness, erotic, horror, and neutral. An experiment of emotion elicitation is also described in this work. Three analysis models applied in our system in order to recognize emotions, including nearest neighbor, discriminant analysis, and multi-layer perception, are discussed in detail. The final test results show that the system has the average recognition rates of 40%, 55.7%, and 77.34% for nearest neighbor, discriminant analysis, and multi-layer perception respectively.

<p dir="ltr">his research investigates the detection of pre-crime events, specifically targeting behaviors indicative of shoplifting, through the advanced analysis of CCTV video data. The study introduces an innovative approach that leverages augmented human pose and emotion information within individual frames, combined with the extraction of activity information across subsequent frames, to enhance the identification of potential shoplifting actions before they occur. Utilizing a diverse set of models including 3D Convolutional Neural Networks (CNNs), Graph Neural Networks (GNNs), Recurrent Neural Networks (RNNs), and a specially developed transformer architecture, the research systematically explores the impact of integrating additional contextual information into video analysis.</p><p dir="ltr">By augmenting frame-level video data with detailed pose and emotion insights, and focusing on the temporal dynamics between frames, our methodology aims to capture the nuanced behavioral patterns that precede shoplifting events. The comprehensive experimental evaluation of our models across different configurations reveals a significant improvement in the accuracy of pre-crime detection. The findings underscore the crucial role of combining visual features with augmented data and the importance of analyzing activity patterns over time for a deeper understanding of pre-shoplifting behaviors.</p><p dir="ltr">The study’s contributions are multifaceted, including a detailed examination of pre-crime frames, strategic augmentation of video data with added contextual information, the creation of a novel transformer architecture customized for pre-crime analysis, and an extensive evaluation of various computational models to improve predictive accuracy.</p>