FACE RECOGNITION SYSTEM USING PRINCIPAL COMPONENT ANALYSIS & LINEAR DISCRIMINANT ANALYSIS METHOD SIMULTANEOUSLY WITH 3D MORPHABLE MODEL AND NEURAL NETWORK BPNN METHOD.

Authors

  • Vikas N.Nirgude Vaishali N.Nirgude, Hitesh Mahapatra, Sandip A.Shivarkar Author

Keywords:

fisherface, PCA, LDA, training, algorithm,image, processing system, real time, morphable, eignface, BPNN

Abstract

This paper proposes a face recognition technique that effectively combines principle components analysis (PCA) and Fisherface algorithm (LDA). PCA use as dimension reduction and Fisherface algorithm as a class specific method is robust about variations such as lighting and different angle Condition. We use 3D morphable model to convert 2D image into 3D image & we can derive multiple image by different lighting and can be rotated to generate multiple images in different poses, the PCA method reduces dimensionality& LDA for the classification. Also recognition is done by using back propagation neural network. In comparison with a conventional method the proposed approach could obtain satisfactory results in the perspectives of recognition rates and speeds.

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Published

2017-01-11

Issue

Section

Articles

How to Cite

FACE RECOGNITION SYSTEM USING PRINCIPAL COMPONENT ANALYSIS & LINEAR DISCRIMINANT ANALYSIS METHOD SIMULTANEOUSLY WITH 3D MORPHABLE MODEL AND NEURAL NETWORK BPNN METHOD. (2017). Global Journal of Advanced Engineering Technologies and Sciences, 4(1), 1-6. https://gjaets.com/index.php/gjaets/article/view/127

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