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A novel face recognition system based on combining eigenfaces with fisher faces using wavelets

Authors
Publisher
Elsevier B.V.
Publication Date
Volume
2
Identifiers
DOI: 10.1016/j.procs.2010.11.007
Keywords
  • Principal Component Analysis (Pca)
  • Linear Discriminant Analysis (Lda)
  • Multi Layer Perceptron (Mlp)
  • Wavelet Fusion
Disciplines
  • Computer Science

Abstract

Abstract In this paper, a biometric system is proposed based on facial features. This proposed system uses an appearance based face recognition method called 2FNN (Two-Feature Neural Network). PCA and LDA are two different feature extraction algorithms used to extract facial features, and then these extracted features are combined using wavelet fusion. The proposed system uses neural networks to classify facial features. Major modules of the proposed system are: extract images from the database; preprocess the extracted images; feature extraction using PCA; feature extraction using LDA; wavelet fusion of the extracted features from PCA and LDA; and neural network based classification. Features are extracted using both PCA and LDA to improve capability of LDA when few samples of images are available. Wavelet fusion and neural networks are used to improve the classification accuracy. The proposed system shows improvements over the existing methods. Preliminary experimental results have shown high accuracy of the system in terms of the correct recognition rate (98.50%) and the equal error rate (1.50%).

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