# Attention-Guided Network Model for Image-Based Emotion Recognition
## 1 Introduction  
Neural networks have shown the ability to outperform traditional approaches in different computational tasks, especially classification. Popular pre-trained network models include AlexNet [1], VGG16 [2], and residual networks such as ResNet50 [3]. However, with the rise in their popularity there are still many unknowns when it comes to the integral learning processes of the network when making appropriate decisions, which is why they are typically regarded as black boxes [4]. To try and understand the decision process, explainable artificial intelligence (XAI) models have been introduced. These models use prediction visualization techniques to visualize the regions of influence from the image on the predicted outcome at different layers within the architecture. XAI helps in understanding which region the network based its decision upon, and based on this knowledge changes in the hyper-parameters can be made to improve the results. This fine-tuning of parameters is an exhaustive time-consuming process for the identification of optimal settings. However, with the introduction of attention modules in recent years [5; 6; 7; 8; 9], a shortcut to extensive parameter tuning for better robustness and regions of focus has become available. These attention modules can be easily integratedinto any existing network architecture to improve network representation and regions of focus by concentrating on informative features and diminishing less important ones [5,6]. One of the well-known and basic applications of machine learning is emotion intelligence [10]. In this study, an attention module developed for facial emotion recognition (FER) is examined for the performance improvement of a base convolutional neural network (CNN) model. The face is chosen for emotion recognition as it is estimated that 65% of emotions are expressed nonverbally via facial expressions (55%) and physiological signals (10%) [11]. In this case, an efficient, robust FER system is desired to create a feedback variable for integration in a closed-loop system to help treat people with autism spectrum disorder (ASD) [12]. While facial expressions are often emphasized as primary nonverbal communicators of emotion, it should be mentioned that whole-body expressions also hold equal importance in understanding others' emotional states, offering distinct advantages such as recognizing emotions when faces are occluded and powerfully conveying action intentions, as shown in previous research [13,14]. The dynamics of emotional stimuli, particularly their role and influence in clinical contexts, has evolved in terms of our understanding over the years. It was widely accepted that emotional stimuli, especially those perceived as threats, yielded considerable power in directing selective attention, prioritizing their processing, and consequently inducing automatic reactions [15,16]. However, recent research offers an intriguing perspective, noting individuals' reactions to such stimuli are incited primarily when they align with personal objectives. This result suggests a context-dependent effect of facial emotions [17,18]. For instance, a previous study [18] explored this concept through a go/no-go task, revealing that emotional cues influenced participants' behavior only when pertinent to the task at hand. Parallel findings have been observed in other domains, such as reaching arm movements and critical executive functions like inhibitory control [17,19]. These revelations underscore an essential nuance: the attentional system's interaction with emotions is intricately tied to its immediate context and the goals of the individual. Consequently, the impact of information on emotion recognition might be contingent upon its relevance to the task in focus. However, given this distinction, the focus of this article is aligned to staged emotions rather than spontaneous reactions to stimuli.