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.

Face detection and recognition with 180 degree rotation based on principal component analysis algorithm

“…In [14] face recognition is proposed with a new method named convolution neural network (C2D CNN). In [15], Introduced a system of facial recognition established adaptive neuro-fuzzy inference. In [16], neuralfuzzy has been used in face recognition and back propagation (BP) algorithm used to update weights of the neurons.…”

Improved Facial Identification Using Adaptive Neuro-Fuzzy Logic Inference System

“…FR is a pattern recognition method that is connected to the notion of artificial intelligence. It is a difficult challenging topic of research since real-time face pictures are created by the merging of numerous components under different settings such as changes in lighting, interference of background, facial position fluctuation, and so on (2)(3)(4)(5) . The two most crucial processes in our automated FR system are feature extraction and classification (6)(7)(8) .…”