Abstract: This paper proposes a face recognition technique that effectively combines principle components analysis (PCA) and Fisherface algorithm (LDA). PCA use as dimension reduction and Fisherface algorithm as a class specific method is robust about variations such as lighting and different angle Condition. We use 3D morphable model to convert 2D image into 3D image & we can derive multiple image by different lighting and can be rotated to generate multiple images in different poses, the PCA method reduces dimensionality& LDA for the classification. In comparison with a conventional method the proposed approach could obtain satisfactory results in the perspectives of recognition rates and speeds.

• Publication Date: 03-Jun-2011
• DOI: 10.15224/978-981-07-1847-3-1027
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Abstract: One of the most important tasks performed in proteomics is correct analysis and interpretation of 2D electrophoresis gel images. The nonlinearities in the gel formation and image acquisition process leads to distortions, overlapped protein spots, saturated spots, faint spots, nonlinear intensity and uneven background in the gel images. As a result, pre-processing and post processing steps are very challenging tasks for precise protein identification. The pre-processing step includes the task of removing additive or multiplicative noises and edge enhancement. The solutions available in the literature have failed to give satisfactory results. Non-separable wavelet processing methods, due to their inherent ability to be able to better represent directional information, seem to be promising for the processing of 2D gel images. In this paper, we explore approaches based on quincunx non-separable filter-banks for image pre-processing and post-processing. A novel method for edge preserved de-

• Publication Date: 12-Mar-2012
• DOI: 10.15224/978-981-07-1847-3-829
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