A hybrid learning RBF neural network for human face recognition with pseudo Zernike moment invariant

2003 ◽  
Author(s):  
J. Haddadnia ◽  
M. Ahmadi ◽  
K. Faez
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Rbf Neural Network ◽  
Hybrid Learning ◽  
Zernike Moment ◽  
Human Face ◽  
Moment Invariant ◽  
Human Face Recognition ◽  
Pseudo Zernike Moment

AN EFFICIENT HUMAN FACE RECOGNITION SYSTEM USING PSEUDO ZERNIKE MOMENT INVARIANT AND RADIAL BASIS FUNCTION NEURAL NETWORK

2003 ◽  
Vol 17 (01) ◽  
pp. 41-62 ◽  
Author(s):  
JAVAD HADDADNIA ◽  
KARIM FAEZ ◽  
MAJID AHMADI
Keyword(s):  
Neural Network ◽  
Extraction Method ◽  
Recognition Rate ◽  
Irrelevant Information ◽  
Zernike Moment ◽  
Human Face ◽  
Moment Invariant ◽  
Feature Extraction Method ◽  
Pseudo Zernike Moment ◽  
Face Localization

This paper introduces a novel method for the recognition of human faces in two-dimensional digital images using a new feature extraction method and Radial Basis Function (RBF) neural network with a Hybrid Learning Algorithm (HLA) as classifier. The proposed feature extraction method includes human face localization derived from the shape information using a proposed distance measure as Facial Candidate Threshold (FCT) as well as Pseudo Zernike Moment Invariant (PZMI) with a newly defined parameter named Correct Information Ratio (CIR) of images for disregarding irrelevant information of face images. In this paper, the effect of these parameters in disregarding irrelevant information in recognition rate improvement is studied. Also we evaluate the effect of orders of PZMI in recognition rate of the proposed technique as well as learning speed. Simulation results on the face database of Olivetti Research Laboratory (ORL) indicate that high order PZMI together with the derived face localization technique for extraction of feature data yielded a recognition rate of 99.3%.

scholarly journals Home Security using Face Recognition Technology

2019 ◽  
Vol 9 (2) ◽  
pp. 678-682
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Personal Identity ◽  
Face Detection ◽  
Personal Characteristics ◽  
Personal Identification ◽  
Human Face ◽  
Human Face Recognition ◽  
Home Security ◽  
Recognition Pattern

Face is the easiest way to penetrate each other's personal identity. Face recognition is a method of personal identification using the personal characteristics of an individual to decide the identification of a person. The method of human face recognition consists basically of two levels, namely face detection and face recognition. There are three types of methods that are currently popular in the developed face recognition pattern, those are Eigen faces algorithm, Fisher faces algorithm and CNN neural network for face recognition

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