Face recognition using Oriented Laplacian of Gaussian (OLOG) and Independent Component Analysis (ICA)
| Autor: | S. Sandeep Inamdar, N. Sanjay Talbar, J. Kailash Karande |
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| Rok vydání: | 2012 |
| Předmět: |
business.industry
Feature vector Feature extraction Pattern recognition Blob detection Facial recognition system Independent component analysis Euclidean distance Computer Science::Computer Vision and Pattern Recognition Principal component analysis FastICA Artificial intelligence business Mathematics |
| Zdroj: | DICTAP |
| Popis: | The problem of face recognition using Laplacian pyramids with different orientations and independent components is addressed in this paper. The edginess like information is obtained by using Oriented Laplacian of Gaussian (OLOG) methods with four different orientations (0°, 45°, 90°, and 135°) then preprocessing is done by using Principle Component analysis (PCA) before obtaining the Independent Components. The independent components obtained by ICA algorithms are used as feature vectors for classification. The Euclidean distance (L2) classifier is used for testing of images. The algorithm is tested on two different databases of face images for variation in illumination, facial expressions and facial poses up to 180° rotation angle. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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