Improved Facial Recognition Algorithms Based on Dragonfly and Grasshopper Optimization

In this paper, we investigate two relatively new optimization algorithms in facial recognition, the grasshopper optimization algorithm (GOA) and binary dragonfly algorithm (BDA) which had the best performance out of 13 optimization algorithms that were compared. We investigate the effectiveness of both optimization algorithms alongside two classifiers, k-nearest neighbor (KNN) and support vector machine (SVM). Performance evaluation of the four combinations, BDA-KNN, BDA-SVM, GOA-KNN and GOA-SVM, indicate near-ideal recognition rates, with the GOA variants slightly outperforming their BDA counterparts. When compared to other recently proposed facial recognition approaches, the proposed algorithms depict improved accuracy.