Design of Face Recognition System by Using Neural Network with Discrete Cosine Transform and Principal Component Analysis
Rohit Jain, Rajshree Taparia
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.
Keywords
Artificial Neural Network, Self-Organizing map, Two dimensional discrete cosine transform, Principal component analysis, unsupervised.
Cite this article
.Design of Face Recognition System by Using Neural Network with Discrete Cosine Transform and Principal Component Analysis . International Journal of Advanced Computer Research. 2012;2(7):66-69.