Neural network based face recognition by using diffraction pattern sampling with a digital ring–wedge detector (original) (raw)

Face Recognition Using Fourier Descriptor & FFNN

Fourier descriptor and feedforward neural network for face recognition. Anatysis is done for various numbers of iterations. Comparison shows that faces are recognized with FFNN mope accurately with 50000 iterations. f,'or experin;ent, FERET database is used.

Design Suitable Neural Network for Processing Face Recognition

Face recognition technology using neural network is an attractive solution for the researchers who are working on the field of machine recognition, pattern recognition and computer vision, in this paper neural network is designed for face recognition using high performance training algorithms based on standard numerical optimization techniques. That is demonstrating how a face recognition system can be designed by artificial neural network. Keywords: Artificial neural network, Face recognition, Training neural networks.

Face Recognition Using Neural Network: A Review

Face recognition from the real data, capture images, sensor images and database images is challenging problem due to the wide variation of face appearances, illumination effect and the complexity of the image background. Face recognition is one of the most effective and relevant applications of image processing and biometric systems. In this paper we are discussing the face recognition methods, algorithms proposed by many researchers using artificial neural networks (ANN) which have been used in the field of image processing and pattern recognition. How ANN will used for the face recognition system and how it is effective than another methods will also discuss in this paper. There are many ANN proposed methods which give overview face recognition using ANN. Therefore, this research includes a general review of face detection studies and systems which based on different ANN approaches and algorithms. The strengths and limitations of these literature studies and systems were included, and also the performance analysis of different ANN approach and algorithm is analysing in this research study.

Recognition of Face Using Neural Network

2015

Advancement in Artificial Intelligence has lead to the developments of various “smart” devices. The task of face Recognition has been actively researched in recent years. Wide usage of biometric information for person identity verification purposes, terrorist acts prevention measures and authentication process simplification in computer systems has raised significant attention to reliability and efficiency of biometric systems. Modern biometric systems still face much reliability and efficiency related issues such as database search speed, errors while recognizing of biometric information or automating biometric feature extraction. In face recognition, many methods are used but due to advancement there are some new methods and algorithm used for recognition of face i.e. line edges map, support vector machine etc. for face recognition. A number of current face recognition algorithms use face representations found by supervised and unsupervised statistical methods. In this paper we us...

Artificial neural network-based face recognition

Control, Communications and Signal …, 2004

This document demonstrates how a face recognition system can be designed with artificial neural network. Note that the training process did not consist of a single call to a training function. Instead, the network was trained several times on various input ideal and noisy images, the images which contents faces. In this case training a network on different sets of noisy images forced the network to learn how to deal with noise, a common problem in the real world.

Some experiments on face recognition with neural networks

NATO ASI SERIES F COMPUTER …, 1998

Abstract. This paper presents some results on the possibilities offered by neural networks for human face recognition. In particular, two algorithms have been tested: learning vector quantization (LVQ) and multilayer perceptron (MLP). Two different approaches have been taken for ...

Face Recognition by Using Neural Network

Acta Informatica Malaysia

Now a day's security is a big issue, the whole world has been working on the face recognition techniques as face is used for the extraction of facial features. An analysis has been done of the commonly used face recognition techniques. This paper presents a system for the recognition of face for identification and verification purposes by using Principal Component Analysis (PCA) with Back Propagation Neural Networks (BPNN) and the implementation of face recognition system is done by using neural network. The use of neural network is to produce an output pattern from input pattern. This system for facial recognition is implemented in MATLAB using neural networks toolbox. Back propagation Neural Network is multi-layered network in which weights are fixed but adjustment of weights can be done on the basis of sigmoidal function. This algorithm is a learning algorithm to train input and output data set. It also calculates how the error changes when weights are increased or decreased. This paper consists of background and future perspective of face recognition techniques and how these techniques can be improved.

FACE RECOGNITION USING NEURAL NETWORK

Although the distinction between optimum decision and pre-processing or feature extraction is not essential, the concept of functional breakdown provides a clear picture for the understanding of the pattern recognition problem. Correct recognition will depend on the amount of discriminating information contained in the measurements and the effective utilization of this information. In some applications, contextual information is indispensable in achieving accurate recognition. For instance, in the recognition of cursive handwritten characters and the classification of fingerprints, contextual information is extremely desirable. When we wish to design a pattern recognition system which is resistant to distortions, flexible under large pattern deviations, and capable of self-adjustment, we are confronted with the adaptation problem. There are many interesting problems that remain in the area of face recognition.

Face Recognition in Fourier Space

This paper describes a simple face recognition system based on an analysis of faces via their Fourier spectra. Recognition is done by finding the closest match between feature vectors containing the Fourier coefficients at selected frequencies. The introduced method compares favourably to three other competing approaches implemented on the same database.