Human face recognition using fuzzy multilayer perceptron

Abstract

In this work a novel method for human face recognition that is based on fuzzy neural network has been presented. Here, Gabor wavelet transformation is used for extraction of features from face images as it deals with images in spatial as well as in frequency domain to capture different local orientations and scales efficiently. In face recognition problem multilayer perceptron (MLP) has already been adopted owing to its efficiency, but it does not capture overlapping and nonlinear manifolds of faces which exhibit different variations in illumination, expression, pose, etc. A fuzzy MLP on the other hand performs better than an MLP because fuzzy MLP can identify decision surfaces in case of nonlinear overlapping classes, whereas an MLP is restricted to crisp boundaries only. In the present work, a new approach for fuzzification of the feature sets obtained through Gabor wavelet transforms has been discussed. The feature vectors thus obtained are classified using a newly designed fuzzified MLP. The system has been tested on a composite database (DB-C) consisting of the ORL face database and another face database created for this purpose and a recognition rate of 97.875% with fuzzy MLP against a recognition rate of only 81.25% with MLP whose feature vectors were also obtained through same Gabor wavelet transforms has been obtained.

Exploration and Analysis of Computational Learning Algorithms on the Second Generation Neural Network

“…For supporting the complex domain (second generation neural network) analysis of various representation methods have been developed [1,3,5,6,8,10,12,14]. Further the application of various computational learning algorithms which have been fully proved and introduced by many developers [15,16,20,21,27,29]. Next the above whole ideas is extended in the practical domain and the experiment is done over the two real datasets problems (monk and wine) with the help of these computational learning methods and fusion with the application of functional modular neural network separately in respective domain (RVNN/CVNN) for evaluating the performance.…”

“…Bhattacharjee [15] has proven the superiority of computational intelligence techniques over conventional statistical methods. In order to solve real domain problems in complex domain, we introduced a combination of two algorithm viz.…”

Heterogeneous face matching using geometric edge-texture feature ( GETF ) and multiple fuzzy-classifier system

“…Often human reasoning is also somewhat fuzzy. Therefore, fuzzy logic is used in many pattern recognition and image processing systems [37][38][39][40][41]. Similarly, classification performance can be improved by using fuzzy logic based classifiers, because fuzzy logic can reduce the uncertainty present in crisp classification.…”

Learning a Backpropagation Neural Network With Error Function Based on Bhattacharyya Distance for Face Recognition

“…Experiments show that projecting the data onto a random lower-dimensional subspace yields results and give an acceptable face recognition rate. Bhattacharjee et al developed in 2009 a face recognition system using a fuzzy multilayer perceptron using back propagation [15].…”