## 2 Motivation and Related Work  
High dimensional datasets often impose computational overheads especially in cases where achieving a near perfect classification accuracy score is the primary concern. Hence, researchers focus mainly on reducing the dimensions of feature sets by filtering out irrelevant features that do not have a significant impact on the classification process. The overheads imposed by high dimensional feature sets mainly include time and space overheads [9; 10; 23]. Nowadays--especially in fields of bioinformatics, medicine and genetics [24], which include features or genes denoted in the form of microarray data of humongous dimension--FS is a necessity in countering the underlying challenges of space and execution time. Many efficient algorithms have been established for this purpose. With an objective to serve the same purpose in FER domain, we have established a FS algorithm to increase the efficiency of the machine learning algorithm and reducing the dimension of the feature set simultaneously. We developed a novel HSA-based FS technique--SFHSA--and applied it on the FER datasets and demonstrated a clear comparison in juxtaposition with other established FS techniques.  
In FER, the features are obtained from datasets having a large dimension. However, there are many redundant features present within the feature sets that bring down the classification accuracy. To eradicate those redundant and irrelevant features as well as make the predictions more accurate, we have taken the initiative to apply our proposed FS technique over FER datasets. The features, extracted from facial expression images include uLBP, hvnLBP, Gabor filters, HOG and PHOG. There are few FS works found in the literature on FER. In [25], a wrapper-based approach to FS using genetic algorithm (GA) was applied to features for FER, obtained using log-Gabor filters. A linear programming technique was used for FS in [26], where the features for FER was extracted using Gabor filters. A FS based on random forest classifiers was incorporated in the FER system where visual-appearance based features were extracted from Gabor filter bank in [27]. For the FS, the mRMR method based on mutual information (MI) quotient was used. [PERSON] et al. in [28] use the combination of fixed filters and trainable non-linear 2D filters based on the biologic mechanism of shunting inhibition. Finally, FS was performed using MI and class separation scores. The work described in [29] proposes automatic FER where features were generated using methods like Gabor filters, log Gabor filter, LBP operator, higher-order local autocorrelation and higher order local autocorrelation-like features. A self-learning attribute reduction algorithm was proposed for FS in [32] which is based on rough set and domain oriented data-driven data mining. The authors in [33] describe an efficient FS technique, late-hill-climbing-based memetic algorithm (LHCMA) on feature sets for FER, which indeed outperforms many previously established FS algorithms. A facial expression recognition system with hvnLBP-based feature extraction and micro GA--along with the particle swarm optimization (PSO)-based feature optimization technique--was proposed in [34] by [PERSON] et al. The FER method, based on a wrapper-based FS technique called multi-objective differential evolution algorithm, was proposed in [35]. The evaluation was done using support vector machine (SVM) classifiers. We have seen that HSA has provided satisfactory results in the case of FS for holistic Bangla word recognition [36], digit classification [37], email classification [38], epileptic seizure detection [39] and protein sequence classification [40]. However, to best of our knowledge, to date, the HSA-based FS technique has not been applied to FER systems. This has served as a motivation for us to perform the SFHSA for FER systems.