Face Recognition System using Artificial Neural Networks Approach

Abstract

Advances in face recognition have come from considering various aspects of this specialized perception problem. Earlier methods treated face recognition as a standard pattern recognition problem; later methods focused more on the representation aspect, after realizing its uniqueness using domain knowledge; more recent methods have been concerned with both representation and recognition, so a robust system with good generalization capability can be built by adopting state-of-the-art techniques from learning, computer vision, and pattern recognition. A face recognition system based on recent method which concerned with both representation and recognition using artificial neural networks is presented. This paper initially provides the overview of the proposed face recognition system, and explains the methodology used. It then evaluates the performance of the system by applying two (2) photometric normalization techniques: histogram equalization and homomorphic filtering, and comparing with Euclidean Distance, and Normalized Correlation classifiers. The system produces promising results for face verification and face recognition.

Real Time Access Control Based on Face Recognition

“…( [1], [22], [23]) Using the method of identifying physical features of humans, biometric technology has a wide range of use in security systems, and is considered one of the safest methods. In Figure 1, we show the classifications of biometrics.…”

“…( [1], [3], [4]) During the past few years, it has become necessary to have a reliable security system, which can secure our assets in the best and safest way possible. Traditional security systems require the user a key, a security password, an RFID card, or and ID card to have access to the system.…”

Evolutionary fusion of a multi-classifier system for efficient face recognition

“…The problem of face recognition involves comparing an input face against models of faces that are stored in a database of known faces and indicating if a match is found. Successful approaches include appearance based methods such as direct correlation, eigenface, and fisher face [1], EBGM [2], active appearance model [3], discriminant analysis [4], local Gabor binary patterns [5], and artificial neuron networks [6]. However most systems are highly sensitive to environmental factors during image capture, such as variations in lighting conditions, and hence cannot meet the real time constraint.…”

Face Recognition: A Survey

“…ANN [86][87] recognizes the face through learning and previous experience. NN based system is trained to recognize the faces.…”