## 4 Proposed Work
This was done with an aim to achieve higher accuracy scores and also to make convenient to compare with the past experimental results. In Table 3, detailed outcomes for different FS techniques on feature sets extracted using uLBP method were provided in terms of both reduced-feature dimensions and recognition accuracy. Tables 4-7 show the similar comparison of the different FS techniques on feature sets extracted using hvnLBP, Gabor-filter-based, HOG and PHOG feature vectors respectively. In case of uLBP, hvnLBP, Gabor-filter-based, HOG and PHOG features, our proposed SFHSA algorithm produces reduced-feature sets which were 65%, 67%, 82%, 69% and 60% less than the original-feature vectors, respectively, and also increased the recognition accuracies up to 17%, 24%, 20%, 18% and 25%, respectively, than the original ones. Thus, it can be concluded that SFHSA demonstrates the best performance in case of PHOG features in terms of accuracy score and dimension reduction for both JAFFE and RAFD datasets. Table 6 reflects the detailed outcomes after applying different optimization techniques on PHOG features. The content of the HM was presented to provide a better understanding of reduced-feature subsets obtained at the end of execution of algorithm. This shows how the HM appears after the SFHSA was executed on feature vectors. In this regard, it is worth mentioning that for all other feature vectors, we have obtained similar content in the HM. Tables 3-7 also highlight the detailed comparative results observed in the present experiment with some other standard optimization algorithms such as simulated annealing (SA), GA, memetic algorithm (MA), mutation enhanced binary particle swarm optimization (ME-BPSO) [63], whale optimization algorithm-crossover mutation (WOA-CM) [64] and LHCMA [33]. The achieved reduced-feature sets along with highest accuracy were marked in bold in the Table Analyzing the observed outcomes, it can be said that our SFHSA-based FS technique has surpassed the above mentioned techniques. It can be observed that there was a significant increment in accuracy score as compared to previous techniques. Out of 30 cases, our proposed technique achieves finer results in 16 cases as compared to all other techniques (2 cases in Gabor-filter-based feature vectors (up to 8% increment), 3 cases of HOG feature vectors (up to 2.60% increment), 4 cases of PHOG feature vectors (up to 3.60% increment), 3 cases of hvnLBP feature vectors (up to 9% increment) and in 4 cases of uLBP feature vectors (up to 2.40% increment). Thus, it is evident that our technique of feature optimization reduced feature dimensionality and improved recognition accuracy. Proposed SFHSA performs quite well against wrapper-based algorithms. This supports the effectiveness of the proposed method. It was observed from the preceding experiment that the second and third best performing algorithms (in terms of accuracy scores) were LHCMA, WAO-CM. Therefore, we have also presented the performance comparison of these techniques with our proposed SFHSA (which achieves the highest accuracy score) in terms of three well-known statistical parameters, namely precision, recall and F-measure. This is done in order to enhance the clarity of the comparison. Tables 8-12 present the performance comparison of SFHSA with respect to LHCMA and WAO-CM for uLBP, hvnLBP, Gabor filter, HOG and PHOG features respectively. The comparison demonstrates that SFHSA has outperformed the rest two techniques in 16 out of 30 cases, from which a very high capability of FS of our technique can be inferred. For each feature vector, we have presented the best reduced-feature set in term of both achieved recognition accuracy and reduced dimensionality out of all the HMS (=15) feature subsets in HM. Since, we have obtained the best results for PHOG features, considering facial images of dimension \(32\times 32\), so through the visual presentations of Figures 7 and 8, we have shown the different reduced-feature sets with recognition accuracies for both JAFFE and RAFD databases respectively.