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Design of Face Recognition System by Using Neural Network with Discrete Cosine Transform and Principal Component Analysis

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Author(s): Rohit Jain, Rajshree Taparia

Journal: International Journal of Advanced Computer Research
ISSN 2249-7277

Volume: 2;
Issue: 7;
Start page: 66;
Date: 2012;
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Keywords: Artificial Neural Network | Self-Organizing map | Two dimensional discrete cosine transform | Principal component analysis | unsupervised.

ABSTRACT
This research paper deals with the implementation of face recognition system using neural network Importance of face recognition system has speed up in the last few decades. A face recognition system is one of the biometric information processing. The developed algorithm for the face recognition system formulates an image-based approach, which uses the Two-Dimensional Discrete Cosine Transform (2D-DCT) for image compression and the Self- Organizing Map (SOM) Neural Network for recognition purpose, simulated in MATLAB. By using 2D-DCT we extract image vectors and these vectors becomes the input to neural network classifier, which uses self-organizing map, algorithm to recognize familiar faces (trained) and faces with variations in expressions, illumination changes, tilt of 5 to 10 degrees. Again face Recognition system is developed with principal component analysis (PCA) instead of Two Dimensional Discrete Cosine Transform (2D-DCT) and self-Organizing Map (SOM) Neural Network for recognition purpose. The crux of proposed algorithm is its beauty to use unsupervised single neural network as classifier.
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