Abstract
Research in the field of face recognition has been popular for several decades. With advances in technology, approaches to solving this problems haves changed. Main goal of this paper was to compare different training algorithms for neural networks and to apply them for face recognition as it is a n
Highlights
People have used different body traits such as voice, face, gait cycle etc. for centuries, in order to recognize other people
The first method is based on the use of deformable templates and extensive mathematics for extraction of the feature vectors
Different models of Artificial Neural Network (ANN) have been used for face recognition, and reason for that is ability of those models to resemble the way in which neurons function in human brain
Summary
People have used different body traits such as voice, face, gait cycle etc. for centuries, in order to recognize other people. Two major parts of all face recognition algorithms are: (1) face detection and normalization and (2) face identification. Detection and segmentation of the face areas from the background is the first step in this process [3] With this successfully done, one of the available algorithms (or developed new) is applied in order to determine whether a face belongs to the known or not known faces of the available database. The second method is based on PCA method In this method, key information is derived from the entire face image. Different models of ANN have been used for face recognition, and reason for that is ability of those models to resemble the way in which neurons function in human brain. Mohamed et al [10] proposed face detection system for image segmentation that depends on skin color
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