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This study is concerned with a design of a face recognition algorithm realized based on feature extraction with the aid of the 2-directional 2-dimensional linear discriminant analysis referred to as (2D) 2 LDA. The 2DLDA algorithm yields high accuracy, but comes with an unresolved problem of handling large feature matrices present in face recognition problems. The proposed (2D) 2 LDA algorithm is computationally more efficient and produces more powerful discrimination decision. The proposed P-RBF NNs is used as a recognition module. The architecture of this module consists of three functional components. The coefficients of the P-RBF NNs model are obtained by fuzzy inference method forming the inference part of fuzzy rules. The essential design parameters (including learning rate, momentum, fuzzification coefficient, and the feature selection mechanism) of the networks are optimized by means of differential evolution (DE). The experimental results reported for benchmark face datasets – the Yale, ORL database, and IC&CI dataset demonstrate the effectiveness and efficiency of (2D) 2 LDA algorithm compared with other pre-processing approaches such as LPP, 2D-PCA, 2D-LPP, SR, PCA, (2D) 2 PCA and fusion of PCA-LDA. The experimental results show that (2D) 2 LDA based P-RBF NNs achieves higher performance than being reported by other methods. [ABSTRACT FROM AUTHOR]

This study is concerned with the concept of Ensemble Fuzzy Radial Basis Function Neural Networks (EFRBFNN) endowed with clustering techniques, polynomial-based LSE/WLSE learning as well as their optimization implemented by Multi-Objective Particle Swarm Optimization (MOPSO). The network architecture of the proposed EFRBFNN classifier is considered three types of clustering techniques and four forms of regression polynomials. The proposed classifier can not only fully capture the local distribution (feature) information contained in the data, but also choose effective network architecture for improving classification performance and compactness. Three classes of clustering techniques (such as iterative self-organizing data analysis techniques algorithm (ISODATA), affinity propagation (AP), and extended K-means) are considered to effectively produce membership degrees based on feature information extraction suitable to fit data characteristics. Least Square Error Estimation (LSE) and Weighted Least Square Error Estimation (WLSE)-based learning with four types of regression polynomials are considered to estimate to coefficients of polynomials. MOPSO is exploited for choosing the effective architecture among different clustering techniques, polynomials, and polynomial-based learning with multi-objective parametric optimization. MOPSO helps achieve a sound compromise between the preferred classification performance and the compactness realized with the aid of four objective functions such as a) classification accuracy (CA), b) complexity of clustering, c) complexity of polynomial and d) sum of squared coefficients (SSC). The improvement and effectiveness of the proposed network architecture are quantified on a basis of the comprehensive experimental results and also a comparative analysis is offered to demonstrate the superiority of the proposed classifier. [ABSTRACT FROM AUTHOR]