Neuro-fuzzy inference system based face recognition using feature extraction

Abstract

Human face recognition (HFR) is the method of recognizing people in images or videos. There are different HFR methods such as feature-based, eigen-faces, hidden markov model and neural network (NN) based methods. Feature extraction or preprocessing used in first three mentioned methods that associated with the category of the image to recognize. While in the NN method, any type of image can be useful without the requirement to particular data about the type of image, and simultaneously provides superior accuracy. In this paper, HFR system based on neural-fuzzy (NF) has been introduced. In the NN system, backpropagation (BP) algorithm is used to update the weights of the neurons through supervised learning. Two sets of the image have been used for training and testing the network to identify the person. If the test image matches to one of the trained sets of the image, then the system will return recognized. And if the test image does not match to one of the trained sets of the image, then the system will return not recognized. The feature extraction methods used in this paper is Geometric moments and Color feature extraction. The recognition rate of 95.556 % has been achieved. The experimental result illustrations that the association of two techniques that provide better accuracy.