# Attention-Guided Network Model for Image-Based Emotion Recognition
## 4 Discussion
This issue is evident in the results of the confusion matrices from Figure 9, where the AGFER and CBAMFER models showed difficulty in identifying and distinguishing neutral and sadness images from anger, disgust, and fear in the FACES dataset. This latter issue highlights the limitation associated with the large array of human facial expressions compared to the relatively limited images in these datasets, as well as the potential need for much larger labelled datasets. A salient difference between the two datasets lies in how they represent emotional intensity. The FACES dataset is characterized by its focus on overt and distinct emotional expressions, capturing what can be described as well-defined emotional states. In contrast, the second dataset presents a more intricate range, spanning from the subtlest of emotional undertones to pronounced expressions. This depth mirrors the diverse ways emotions manifest in real-life settings, where their intensity can vary widely. Moreover, the cultural representation in each dataset is also markedly different. FACES is primarily anchored in Western contexts, echoing emotional norms and expression styles commonly associated with European societies. Conversely, the OULU-CASIA dataset is more globally inclusive, incorporating data from varied regions such as Asia and Europe, thereby providing a wider lens into cultural emotional expressions. This diverse blend of cultural variations, demographics, emotional intensities, and image quality contributes to the observed effects on model performance during cross-database evaluation. Comparing the AGFER-integrated model with other research approaches, it can be seen that the proposed attention-guidance pipeline yielded state-of-the-art results with the OULU-CASIA dataset. Table 3 represents the evaluation results of different research approaches on the OULU-CASIA dataset. The increase in dimensional space coupled with the proposed attention module and guidance strategy demonstrated effectiveness in improving classification performance for FER. The attention module highlighted the regions of relevance and extracted the descriptive features to find a comprehensive pattern, capturing facial expressions effectively. \begin{table}
\begin{tabular}{l c} \hline \hline
**Approach** & **Mean Accuracy (\%)** \\ \hline [PERSON] et al. [10] & 81.46 \\ [PERSON] et al. [42] & 84.17 \\ [PERSON] et al. [31] & 84.59 \\ [PERSON] et al. [43] & 84.72 \\ [PERSON] et al. [44] & 86.23 \\ [PERSON] et al. [45] & 87.71 \\ [PERSON] et al. [43] (with LSTM) & 88.98 \\ Proposed AGFER approach & **89.19** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of mean classification accuracy results from different methods with the OULU-CASIA dataset. Values in bold represent the best performance. The weighted loss balanced the class presence distribution during training so that each emotion class was represented equally. A crucial aspect warranting discussion is the relationship between the proposed AGFER attention module and the self-attention mechanism foundational to the Transformer architecture. The Transformer's self-attention mechanism has garnered acclaim for its ability to weigh the significance of different input elements in relation to a given element, allowing for dynamic recalibration based on context. Our attention module highlights the feature maps of influence, as well as the focus area within each map, and is coupled with the adopted guidance strategy, thus directing the attention mechanism towards regions of emotional significance. By understanding the underpinnings of both mechanisms, it becomes evident that, while they share foundational similarities in attending to different components of input, the differences in our module are geared towards providing the network with prior knowledge about where the most informative regions are likely to be located. This proactive approach ensures the model's ability to recognize subtle emotional cues, leading to improved accuracy in emotion classification.