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http://dx.doi.org/10.5391/JKIIS.2014.24.2.173

Design of ASM-based Face Recognition System Using (2D)2 Hybird Preprocessing Algorithm  

Kim, Hyun-Ki (Department of Electrical Engineering, The University of Suwon)
Jin, Yong-Tak (Department of Electrical Engineering, The University of Suwon)
Oh, Sung-Kwun (Department of Electrical Engineering, The University of Suwon)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.24, no.2, 2014 , pp. 173-178 More about this Journal
Abstract
In this study, we introduce ASM-based face recognition classifier and its design methodology with the aid of 2-dimensional 2-directional hybird preprocessing algorithm. Since the image of face recognition is easily affected by external environments, ASM(active shape model) as image preprocessing algorithm is used to resolve such problem. In particular, ASM is used widely for the purpose of feature extraction for human face. After extracting face image area by using ASM, the dimensionality of the extracted face image data is reduced by using $(2D)^2$hybrid preprocessing algorithm based on LDA and PCA. Face image data through preprocessing algorithm is used as input data for the design of the proposed polynomials based radial basis function neural network. Unlike as the case in existing neural networks, the proposed pattern classifier has the characteristics of a robust neural network and it is also superior from the view point of predictive ability as well as ability to resolve the problem of multi-dimensionality. The essential design parameters (the number of row eigenvectors, column eigenvectors, and clusters, and fuzzification coefficient) of the classifier are optimized by means of ABC(artificial bee colony) algorithm. The performance of the proposed classifier is quantified through yale and AT&T dataset widely used in the face recognition.
Keywords
ASM; $(2D)^2$Hybrid Preprocessing Algorithm; Face Recognition System; ABC;
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Times Cited By KSCI : 1  (Citation Analysis)
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