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Combination of wavelet and PCA for face recognition

Mazloom, M ; Sharif University of Technology | 2006

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  1. Type of Document: Article
  2. DOI: 10.1109/IEEEGCC.2006.5686204
  3. Publisher: 2006
  4. Abstract:
  5. This work presents a method to increase the face recognition accuracy using a combination of Wavelet, PCA, and 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 and PCA. During the classification stage, the Neural Network (MLP) 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 based method on the Yale and ORL face databases. Moreover, the accuracy of the proposed method is improved
  6. Keywords:
  7. Computational loads ; Feature extraction and classification ; Hybrid approach ; MLP neural networks ; ORL face database ; PCA ; Recognition accuracy ; Robust decisions ; Feature extraction ; Neural networks ; Wavelet transforms ; Face recognition
  8. Source: 2006 IEEE GCC Conference, GCC 2006, Manama, 20 March 2006 through 22 March 2006 ; 2006 ; 9780780395909 (ISBN)
  9. URL: https://ieeexplore.ieee.org/abstract/document/5686205