Loading...
Wavelet transform and fusion of linear and non linear method for face recognition
Mazloom, M ; Sharif University of Technology
878
Viewed
- Type of Document: Article
- DOI: 10.1109/DICTA.2009.56
- Abstract:
- This work presents a method to increase the face recognition accuracy using a combination of Wavelet, PCA, KPCA, and RBF Neural Networks. Preprocessing, feature extraction and classification rules are three crucial issues for face recognition. This paper presents a hybrid approach to employ these issues. For preprocessing and feature extraction steps, we apply a combination of wavelet transform, PCA and KPCA. During the classification stage, the Neural Network (RBF) is explored to achieve a robust decision in presence of wide facial variations. At first derives a feature vector from a set of downsampled wavelet representation of face images, then the resulting PCA-based linear features and KPCA- based nonlinear features on wavelet feature vector for reduces the dimensionary of the vector, are extracted. During the classification stage, the Neural Network (RBF) is explored to achieve a robust decision in presence of wide facial variations. The computational load of the proposed method is greatly reduced as comparing with the original PCA, KPCA, ICA and LDA based method on the ORL, Yale and AR face databases. Moreover, the accuracy of the proposed method is improved. © 2009 IEEE
- Keywords:
- Kernel PC and RBF nueral netwrok ; Wavelet transform Principal Component Analysis (PCA) ; Computational loads ; Face database ; Face images ; Feature extraction and classification ; Feature vectors ; Hybrid approach ; Linear feature ; Non-linear methods ; Nonlinear features ; RBF Neural Network ; Robust decisions ; Wavelet feature vectors ; Wavelet representation ; Feature extraction ; Mathematical transformations ; Neural networks ; Principal component analysis ; Radial basis function networks ; Wavelet transforms ; Face recognition
- Source: DICTA 2009 - Digital Image Computing: Techniques and Applications, 1 December 2009 through 3 December 2009, Melbourne ; 2009 , Pages 296-302 ; 9780769538662 (ISBN)
- URL: http://ieeexplore.ieee.org/document/5384958