Cultural Emperor Penguin Optimizer and Its Application for Face Recognition

Face recognition is an important technology with practical application prospect. One of the most popular classifiers for face recognition is support vector machine (SVM). However, selection of penalty parameter and kernel parameter determines the performance of SVM, which is the major challenge for SVM to solve classification problems. In this paper, with a view to obtaining the optimal SVM model for face recognition, a new hybrid intelligent algorithm is proposed for multiparameter optimization problem of SVM, which is a fusion of cultural algorithm (CA) and emperor penguin optimizer (EPO), namely, cultural emperor penguin optimizer (CEPO). The key aim of CEPO is to enhance the exploitation capability of EPO with the help of cultural algorithm basic framework. The performance of CEPO is evaluated by six well-known benchmark test functions compared with eight state-of-the-art algorithms. To verify the performance of CEPO-SVM, particle swarm optimization-based SVM (PSO-SVM), genetic algorithm-based SVM (GA-SVM), CA-SVM, and EPO-SVM, moth-flame optimization-based SVM (MFO-SVM), grey wolf optimizer-based SVM (GWO-SVM), cultural firework algorithm-based SVM (CFA-SVM), and emperor penguin and social engineering optimizer-based SVM (EPSEO-SVM) are used for the comparison experiments. The experimental results confirm that the parameters optimized by CEPO are more instructive to make the classification performance of SVM better in terms of accuracy, convergence rate, stability, robustness, and run time.