Principal Component Analysis Based Wavelet Transform
The principal component analysis (PCA) is a valuable statistical means, implemented in time domain that has found application in many fields such as face recognition and image compression, and is a common technique for finding patterns in data of high dimension. This paper investigates the ability t...
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| Format: | Article |
| Language: | English |
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Unviversity of Technology- Iraq
2012-05-01
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| Series: | Engineering and Technology Journal |
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| Online Access: | https://etj.uotechnology.edu.iq/article_56925_480f8e23b4119b217bfed2562c91eecf.pdf |
| Summary: | The principal component analysis (PCA) is a valuable statistical means, implemented in time domain that has found application in many fields such as face recognition and image compression, and is a common technique for finding patterns in data of high dimension. This paper investigates the ability to implement PCA in frequency domain, by using the wavelet transform (WT), and evaluate its effectiveness based on face recognition as a means to find patterns in data. The basic idea of frequency domain implementation of the PCA refers to the correlation implementation using wavelet transform. The Min-max is invoked to increase wavelet based eigenface robustness to variations in facial geometry and illumination. Two face images are contrast in terms of their correlation distance. A threshold is used to restrict the impostor face image from being identified. Experimental results point up the effectiveness of a new method in either using varying (noisy images, unknown images, face expressions, illumine, and scales ). |
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| ISSN: | 1681-6900 2412-0758 |