Face recognition using 3D GLCM and Elman Levenberg recurrent Neural Network
Rocky Yefrenes Dillak, Sumartini Dana, Marthen Dangu Elu Beily · 2016
Face recognition has been widely used as biometric systems currently. Because of its ability to recognize a person identity reliably and accurately based on face. This research aims to develop a method that can be used for face recognition system. The proposed approach works as follows:(i) preprocess input images using gray-scaling, contrast stretch and Amoeba Median Filter, (ii) extract its characteristic features using 3D GLCM which is expanded from 2D GLCM, (iii) train these characteristic features using The Elman Levenberg Neural Network to obtain the recognition rate. Data used in this paper were taken from The Essex database which contains 7900 face images taken from 395 individuals (male and female). Based on experiments conducted, this method reached the recognition rate of 98.86 percent which showed the considerable enhancement compared to previous methods.