# Emotion Recognition in Usability Testing: A Framework for Improving Web Application UI Design
## 2 Materials and Methods  
### Emotion Recognition Methods  
In psychology, cognitive science, and neuroscience, there are two main ways to classify and perceive emotions: dimensional or categorical [13]. The dimensional approach usually uses dimensions such as negative vs. positive, calm vs. excited. One of the main dimensions of dimensional models is valence, which reflects the pleasantness or unpleasantness of an emotion [14]. Emotions can range from very positive to very negative, clearly indicating the overall emotional tone. Arousal refers to the intensity or level of activation of an emotion [15]. This dimension ranges from calmness and low arousal to excitement and high arousal. It provides insights into the energy and stimulation associated with different emotional states. Some dimensional models include dominance or control as a third dimension [16]. This dimension reflects the perceived control or influence of the emotion. This adds another layer of complexity to emotional experiences. A categorical approach tends to use separate classes. The first attempts to classify emotions into groups included a large number of emotions in order to represent the complex spectrum of human emotions as accurately as possible. The number of emotions then reached several dozen, but [PERSON] proposed a revolutionary idea [17]: the existence of universal, biologically determined basic emotions shared by various cultures. This laid the groundwork for the development of categorical models, suggesting that there is a set of discrete, cross-cultural emotions that all people experience. A categorical classification of emotions was chosen for this research. Affective computing--the study and development of systems and devices capable of recognizing, interpreting, processing, and simulating human affect, relates to psychological and cognitive science, particularly the classification of emotions [18]. Affective computing uses machine learning techniques that process and classify various sensory input/output channels between computers and humans. The goal of most of these methods is to create labels that match the categories that a person would perceive in the same situation. This paper explores the field of facial expression recognition to recognize the emotions of web application users from facial expressions without using any wearable, physiological sensors so that a person feels comfortable and the situation is as close as possible to everyday use. Facial expression recognition can be used to evaluate the UX of digital products or services. It can also be useful to determine where a person is looking, or what particular object is of great interest to them. Such an approach allows feedback on how users react or enjoy using the system, making it a suitable choice to achieve the objective of this work. The detection and processing of facial expressions is achieved by various methods such as optical flow [19], hidden Markov models [20], neural network processing [21], or active appearance models [22]. Improvements are also being made to facial expression recognition methods [23] to maximize recognition accuracy. It is possible to combine more than one modality (multimodal recognition such as facial expressions and speech prosody [24]; facial expressions and hand gestures [25]; or speech and text [26] for multimodal data and metadata analysis) for a more reliable representation of a subject's emotional state for evaluation. In this research, the convolutional neural network (CNN) is used for emotion recognition. The CNN is often found in emotion recognition models due to its excellent image processing characteristics (Figure 1). The fundamental architecture of CNNs has a special structure that allows efficient processing of grid-structured data such as images [27]. Convolutional layers perform convolution operations in which kernels are swept over the input image, extracting various features such as edges, shapes, and textures [28]. Pooling layers such as maximum pooling reduce dimensions while retaining important information.