Abstract: In various real life applications such as remote sensing and medical image diagnosis image fusion plays imperative role and it is more popular for image processing applications. Because of inadequate nature of practical imaging systems the capture images or acquired images are corrupted from various noise hence fusion of image is an integrated approach where reduction of noise and retaining the original features of image is essential. Image fusion is the process of extracting meaningful visual information from two or more images and combining them to form one fused image. Discrete Wavelet Transform (DWT) has a wide rang of application in fusion of noise images. Previously, real valued wavelet transforms have been used for image fusion. Although this technique has provided improvements over more inhabitant methods, this transform suffers from the shift variance and lack of directionality associated with its wavelet bases. These problems have been overcome by the use of a reversible and

• Publication Date: 09-Jul-2012
• DOI: 10.15224/978-981-07-2950-9-9482
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Abstract: In the field of face recognition, this paper explores a comparison of five most popular algorithms. These algorithms are Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Independent Component Analysis (ICA) , Support Vector Machine (SVM) and Histogram of Gabor Phase Patterns(HGPP). The performance of the algorithms have been measured in terms of the accuracy, training time, testing time, total execution time and memory usage for train and test the databases. The algorithms have been tested on the AT&T and IFD face database. The investigation shows that SVM outperforms the rest of the algorithms.

• Publication Date: 09-Jul-2012
• DOI: 10.15224/978-981-07-2950-9-9487
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