title: Optimized face recognition algorithm using radial basis function neural networks and its practical applications.
abstract: In this study, we propose a hybrid method of face recognition by using face region information extracted from the detected face region. In the preprocessing part, we develop a hybrid approach based on the Active Shape Model (ASM) and the Principal Component Analysis (PCA) algorithm. At this step, we use a CCD (Charge Coupled Device) camera to acquire a facial image by using AdaBoost and then Histogram Equalization (HE) is employed to improve the quality of the image. ASM extracts the face contour and image shape to produce a personal profile. Then we use a PCA method to reduce dimensionality of face images. In the recognition part, we consider the improved Radial Basis Function Neural Networks (RBF NNs) to identify a unique pattern associated with each person. The proposed RBF NN architecture consists of three functional modules realizing the condition phase, the conclusion phase, and the inference phase completed with the help of fuzzy rules coming in the standard 'if-then' format. In the formation of the condition part of the fuzzy rules, the input space is partitioned with the use of Fuzzy C-Means (FCM) clustering. In the conclusion part of the fuzzy rules, the connections (weights) of the RBF NNs are represented by four kinds of polynomials such as constant, linear, quadratic, and reduced quadratic. The values of the coefficients are determined by running a gradient descent method. The output of the RBF NNs model is obtained by running a fuzzy inference method. The essential design parameters of the network (including learning rate, momentum coefficient and fuzzification coefficient used by the FCM) are optimized by means of Differential Evolution (DE). The proposed P-RBF NNs (Polynomial based RBF NNs) are applied to facial recognition and its performance is quantified from the viewpoint of the output performance and recognition rate.
authors.affiliation: Department of Electrical Engineering, The University of Suwon, San 2-2 Wau-ri, Bongdam-eup, Hwaseong-si, Gyeonggi-do, 445-743, South Korea. Electronic address: shyoo@suwon.ac.kr.
authors.forename: Sung-Hoon
authors.initials: SH
authors.lastname: Yoo
authors.affiliation: Department of Electrical Engineering, The University of Suwon, San 2-2 Wau-ri, Bongdam-eup, Hwaseong-si, Gyeonggi-do, 445-743, South Korea. Electronic address: ohsk@suwon.ac.kr.
authors.forename: Sung-Kwun
authors.initials: SK
authors.lastname: Oh
authors.affiliation: Department of Electrical & Computer Engineering, University of Alberta, Edmonton T6R 2V4 AB, Canada; Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, 21589, Saudi Arabia; Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland. Electronic address: wpedrycz@ualberta.ca.
authors.forename: Witold
authors.initials: W
authors.lastname: Pedrycz
mesh_terms: D000465:Algorithms; D056667:Biometric Identification / Q000379:methods / Q000592:standards; D016000:Cluster Analysis; D000066499:Facial Recognition; D006801:Humans; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods* / Q000592:standards*; D010775:Photic Stimulation / Q000379:methods; D025341:Principal Component Analysis
keywords: ASM (Active Shape Model); DE (Differential Evolution); FCM (Fuzzy C-means Method); P-RBF NNs (Polynomial based Radial Basis Function Neural Networks); PCA (Principal Component Analysis)