Face Recognition Using Harmony Search-Based Selected Features

Harmony search algorithm (HSA) is an evolutionary algorithm which is used to solve a wide class of problems. HSA is based on the idea of musician's behavior in searching for better harmonies. It tries to find the optimal solution according to an objective function. HSA has been applied to various optimization problems such as timetabling, text summarization, flood model calibration. In this paper we used HSA to select an optimal subset of features that gives a better accuracy results in solving the face recognition problem. The proposed approach is compared with the standard Principal Component Analysis (PCA). A set of images that each has a face adopted from the literature is used to evaluate the proposed algorithm. The obtained results show that using HSA to select the subset of features gives better accuracy in face recognition.

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