Facial emotion recognition (FER) is a computer vision process aimed at detecting and classifying human emotional expressions. FER systems are currently used in a vast range of applications from areas such as education, healthcare, or public safety; therefore, detection and recognition accuracies are very important. Similar to any computer vision task based on image analyses, FER solutions are also suitable for integration with artificial intelligence solutions represented by different neural network varieties, especially deep neural networks that have shown great potential in the last years due to their feature extraction capabilities and computational efficiency over large datasets. In this context, this paper reviews the latest developments in the FER area, with a focus on recent neural network models that implement specific facial image analysis algorithms to detect and recognize facial emotions. This paper’s scope is to present from historical and conceptual perspectives the evolution of the neural network architectures that proved significant results in the FER area. This paper endorses convolutional neural network (CNN)-based architectures against other neural network architectures, such as recurrent neural networks or generative adversarial networks, highlighting the key elements and performance of each architecture, and the advantages and limitations of the proposed models in the analyzed papers. Additionally, this paper presents the available datasets that are currently used for emotion recognition from facial expressions and micro-expressions. The usage of FER systems is also highlighted in various domains such as healthcare, education, security, or social IoT. Finally, open issues and future possible developments in the FER area are identified.

Digital learning environments create a dynamic and engaging learning and teaching context that promotes a deeper understanding of complex concepts, eases the teaching process and fosters a passion for learning. Moreover, integrating interactive materials into pilot courses can assist teachers in better assessing student learning and adjusting their teaching strategies accordingly. The teachers can also receive valuable insights into students’ strengths and weaknesses, allowing them to provide targeted support and intervention when needed. For students from the defence and security fields, digital learning environments can create realistic simulations and virtual training scenarios that allow students to practise their skills in a controlled and safe environment, develop hands-on experience, and enhance their decision-making abilities without the need for real-world training exercises. In this context, the purpose of this paper is to introduce an approach for solving mathematical problems embedded in technical scenarios within the defence and security fields with the aid of digital technology using different software environments such as Python, Matlab, or SolidWorks. In this way, students can visualise abstract concepts, experiment with different scenarios, and receive instant feedback on their understanding. At the same time, the use of didactic and interactive materials can increase the interest among students and teachers for utilising mathematical models and digital technologies in the educational process. This paper also helps to reinforce key concepts and enhance problem-solving skills, sparking curiosity and creativity, and encouraging active participation and collaboration. Throughout the development of this proposal, based on survey analysis, good practices are presented, and advice for improvement is collected while having a wide range of users giving feedback, and participating in discussions and testing (pilot) short-term learning/teaching/training activities.

The use of didactic and interactive materials is essential in generating interest among students and teachers in utilizing mathematical models and digital technologies for the educational process. These materials create a dynamic and engaging learning environment that promotes a deeper understanding of complex concepts and fosters a passion for learning. Additionally, integrating interactive materials into pilot courses can assist teachers in better assessing student learning and adjusting the teaching strategies accordingly and the teachers can receive valuable insights into students' strengths and weaknesses, allowing them to provide targeted support and intervention when needed. In this context, the purpose of this paper is to introduce an approach for solving mathematics problems embedded in technical scenarios within the defense and security fields with the aid of digital technology. In this way, students can visualize abstract concepts, experiment with different scenarios, and receive instant feedback on their understanding. This paper helps to reinforce key concepts and enhance problem-solving skills, sparking curiosity, creativity, and encouraging active participation and collaboration. Throughout the development of this proposal, good practices are adopted, and advice for improvement is collected while having a wide range of users, giving feedback, discussions, and testing (pilot) short-term learning/teaching/training activities.