Face Recognition Algorithm Based on Improved Artificial Hummingbird Algorithm-Optimized Support Vector Machine

Abstract

Traditional face recognition systems use various bionic algorithms combined with Support Vector Machines (SVM) to form a corresponding face recognition model for the final face classification problem. This method selects the optimal SVM parameters through algorithm iteration. However, this strategy is hindered by low classification accuracy, long training time, and the possibility of easily falling into the local optimal solution. This paper proposes a face recognition method using an improved Artificial Hummingbird Algorithm (AHA) to optimize SVM. First, AHA is improved by introducing a chaotic sequence of Tent mapping so that the hummingbird population is initialized more uniformly and the algorithm does not fall into the local optimal solution; second, the improved AHA algorithm is introduced in the method of face recognition using SVM. By setting a certain number of iterations for the algorithm, the optimal relevant parameters used to optimize SVM are selected to improve face recognition accuracy. The improved AHA is compared to the Grey Wolf Optimizer (GWO), Sparrow Search Algorithm (SSA), and Whale Optimization Algorithm (WOA). The improved AHA has a faster convergence speed in solving the benchmark function. Simultaneously, in a face recognition experiment on the ORL face database, the improved AHA combined with SVM is compared to GWO, SSA and WOA combined with SVM. In face recognition tasks, the improved AHA combined with SVM achieves higher accuracy and recall rate, with a faster inference speed.