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Face Recognition by MATLAB with ORL database

Implement:

1- ICA : Independent Component Analysis.

2- LDA : Linear Discriminant Analysis.

3- SVM : support vector machine.

 A. First experiment: Only one image per person will be used for the training. Thus, the total number of images in the training dataset is 40 images. During this experiment, we will check three different use-cases for the test images:

1- A test image (different from the training images) is used with no added noise.

2- The Gaussian noise is synthesized into to the test images and the result noisy

images are used for testing.

3- The salt and pepper noise is added to the test images similar to the Gaussian noise.

 B. Second experiment: In the second experiment, Only Five images per person will be used for training. Thus, the total number of images in the dataset is 200 images. During this experiment, three different use-cases will be checked for the test images:

1- A test image (different from the training images) is used with no added noise.

2- The Gaussian noise is synthesized into to the test images and the result noisy

images are used for testing.

3- The salt and pepper noise is added to the test images similar to the Gaussian noise.

 C. In each use-case, the three facial recognition algorithms are compared using the following two measures:

1- Accuracy percentage (100%).

2- Execution time (sec).

ORL database

Kemahiran: Algoritma, Kejuruteraan Elektrik, Kejuruteraan, Pengimejan, Matlab and Mathematica

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Tentang Majikan:
( 1 ulasan ) St. John's, Canada

ID Projek: #15906946

Dianugerahkan kepada:

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