Abstract
This paper presents a method of recognizing faces from frontal pose images by using Circularly Orthogonal Moments (COM). In the presented method, first Pseudo Zernike Moment (PZM), Zernike Moment (ZM) and Polar Cosine Transform (PCT) were employed to extract features from the global information of images, and then Radial Basis Function (RBF) Network and Genetic Algorithm (GA) were used for face recognition based on the features that had been already extracted by PZM, ZM, and PCT. Also, the images were preprocessed to enhance their gray-level, which helps to increase the accuracy of recognition. The proposed method was tested with the use of Yale database. The experimental results show that the recognition accuracy of our proposed COM is much higher than that of single feature domain.
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