# Multimodal Emotion Recognition Using Visual, Vocal and Physiological Signals: A Review
## 6 Discussion  
The performance of emotion recognition depends on the type of training data and on whether the data are natural, acted or induced [214]. In addition, methods should implement learning and testing on either diversified or different datasets to test the robustness and ability to learn salient characteristics from the samples. The use of in-the-wild datasets during the development of the emotion recognition model is essential. These data closely mirror real-world scenarios.  
The aim is an architecture of an intelligent sensing machine that can quickly learn a large amount of information with little prior knowledge and adapt in real-time to accommodate new data. The model must be able to accurately classify emotion states that are at the borderline between several other emotions (see continuous model/circumplex model). Progress has been made regarding developing advanced intelligent systems that aim to detect and process emotional information contained in multimodal sources. The additional complexity of the multimodal approach is sufficiently justified in terms of the improved accuracy obtained. However, multimodal systems should be able to implement recognition continuously (e.g., determine the emotional changes) while online and performing in real-time. The multimodal system with a specific set of required modalities should be versatile and function in case some inputs of the required modalities are not available. In addition to improving the model's classification rate, the model should report correct spontaneous emotional states. The validity of correct spontaneous emotion expression presence in both micro-expressions and macro-expressions can be conducted to assess the suggestion of [PERSON] [81], who argues that a spontaneous, genuine expression can be a macro- or micro-expression. The model should also be context-sensitive to adapt to any user [258] and can give a consistent result in any real-world environment. The recognition should also consider the fact that a human can experience different emotions at the same time.  
The exploitation of non-Euclidean space for data representation and machine learning operations is a technological advance that can help to improve emotion recognition. Geometric deep learning methods use the non-Euclidean domain to retrieve the fine details related to the face deformation of each facial expression. They represent the input 3D image on a graph or manifold [259]. A facial emotional expression recognition representing the input data on the Riemannian manifold was proposed and showed promise, though it exhibited a low classification accuracy [260].  
Significant technological advances have been achieved in deep learning design. However, the data and diversity in the data required for the approaches to execute at the desired degree of accuracy in real-world applications remains challenging. Incorporating knowledge representation and reasoning into models is essential for improving generalization across various contexts, scenarios and cultural diversity. This approach can improve emotion recognition's ability to infer and adapt to new, previously unknown data, increasing the robustness of emotion recognition methods. Furthermore, leveraging explainability and interpretability is critical for model adaptation.  
The inclusion of the temporal dimension of data during model training can enhance recognition accuracy. Furthermore, considering dynamic emotions, which account for how emotions change over time, will considerably increase the effectiveness of emotion recognition methods. The emotion recognition accuracy can be improved by considering the evolution of emotions.