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3 January 2020 A non-iterative fuzzy neural classifier for face recognition
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Proceedings Volume 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019); 1137308 (2020) https://doi.org/10.1117/12.2557232
Event: Eleventh International Conference on Graphics and Image Processing, 2019, Hangzhou, China
Abstract
This paper presents a non-iterative fuzzy neural classifier to improve the efficiency and training speed of the automatic face recognition system. Main issue of face recognition is that its performance deteriorates if there are variations like illumination, pose and expressions. In the proposed classifier, principal component analysis (PCA) is used for feature extraction that projects the face image into eigen space that best describes the data with reduce size of the database. These features are fed to non-iterative fuzzy neural classifier in which hidden neurons are randomly generated and output weights are calculated analytically in non-iterative manner. Fuzzy neural classifier offers fuzzy activation function which provides various normalization in face images. Experimental results on Yale Face database demonstrate that the proposed classifier performs well compared to the state-of-art recognition techniques in terms of recognition error rate and learning speed.
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Tripti Goel and Murugan R. "A non-iterative fuzzy neural classifier for face recognition", Proc. SPIE 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019), 1137308 (3 January 2020); https://doi.org/10.1117/12.2557232
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