Design of face recognition system based on fuzzy transform and radial basis function neural networks
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In this study, a face recognition method based on fuzzy transform and radial basis function neural networks is proposed. In order to reduce the dimensionality and extract the important features of face images, fuzzy transform with fuzzy partition techniques is used. Fuzzy radial basis function neural networks (FRBFNNs) are used as a classifier to identify face images into several categories. Radial basis functions are defined by fuzzy C-means clustering method which can analyze the distribution of data points over the input spaces. In order to validate the proposed face recognition system, experimental comparative studies are conducted on the benchmark face datasets such as YALE, ORL, and ABERDEEN databases. A comparative analysis demonstrates that the proposed face recognition system is superior to the conventional face recognition techniques.
KeywordsFuzzy C-means clustering (FCM clustering) Fuzzy transform (F-transform) Fuzzy radial basis function neural networks (FRBFNNs) Preprocessing technique
This work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2017R1D1A1B03032333).
Compliance with ethical standards
Conflict of interest
Seok-Beom Roh, Sung-Kwun Oh, Jin-Hee Yoon, Kisung Seo declare that they have no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
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