# Particle Swarm Optimisation for Emotion Recognition Systems: A Decade Review of the Literature
## 5 Discussion
### Physical Modalities  
Physical modalities are one of the modalities to enable ERS. Most commonly known from previous research related to physical modalities are facial expression and speech recognition. Facial expression is based on the human facial movement, eyebrows, eyes, mouth, and the movement of the face [19], while speech is based on the voice and tone of a person that represents emotions and sentiments [38]. In the study by [46], the authors proposed a facial expression recognition system using the PSO technique with the concept of a micro-genetic algorithm (MGA) to optimise the real-time recognition of facial expressions. Facial expression is considered one of the popular modalities for emotion recognition systems with potential applications [46]. The proposed system consisted of three steps: feature extraction, feature optimisation, and emotion recognition. The MGA-PSO algorithm was used to identify the most significant features of different facial expressions. This algorithm incorporated personal expression and maintained a secondary swarm with a small population size of five to host the swarm leader and four follower particles with the highest/lowest correlations with the leader from the non-replaceable memory to increase global search capabilities [46; 56; 57]. The authors suggested that further improvements, such as using the firefly algorithm and cuckoo search, could be explored, and the multi-objective swarm evolutionary algorithm could also be added to deal with real-world optimisation. Next, the study by [58] focused on feature extraction and classification for facial expressions. They suggested the use of Local Binary Pattern (LBP) as a technique from PSO for facial expression. LBP is successful due to its speed and discrimination performance, especially for low-resolution images. The proposed approach was an improvement over a previous study and used an LBP histogram with different block sizes of a face image as feature vectors. The facial expression was further classified using Principal Component Analysis (PCA). The results of the study showed that using LBP improved the average accuracy by 22% compared to not using LBP. However, a limitation of the study was its generalisation to other datasets.  
Another related study in the field of facial expression recognition is the work by [59], which proposed a Micro-Facial Expression based on Optimal Convolutional Neural Network (MFEOCNN) algorithm. The method involves pre-processing the input using geometric features, Histogram Oriented Gradient (HOG) features, and LBP, then optimising the features using the Modified Lion Optimization (MLO) algorithm. The final step involves using a convolutional neural network (CNN) to recognise facial expressions. The performance of the MFEOCNN algorithm was evaluated in terms of false measure and accuracy, and the results demonstrated that the proposed method outperformed existing methods in terms of recognition accuracy. Other physical modalities that have been found from the SLR are through speech recognition analysis. Ref. [60] proposed the use of metaheuristic analysis based on Ant Colony Optimization (ACO) algorithms, which have been employed in some previous studies. The research results suggest that using ACO could reduce complexity in speech recognition. The ACO approach involves constructing a graph model, then predicting the degree of the generated graph to satisfy the target degree sequence. The proposed method of ACO in speech recognition has advantages over complete graphs in terms of reduced complexity and more flexibility in the search space compared to the binary-connected graph model.