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| author | nunzip <np.scarh@gmail.com> | 2018-11-16 17:32:58 +0000 |
|---|---|---|
| committer | nunzip <np.scarh@gmail.com> | 2018-11-16 17:32:58 +0000 |
| commit | 702cbeec081f884d336c5b61b8717b9df5b4c48b (patch) | |
| tree | d0b6cc9e3a083b39c437ac1926dfd48cd880e717 | |
| parent | 55e4c2c148c1e4b0714671da774954d739548fb6 (diff) | |
| download | vz215_np1915-702cbeec081f884d336c5b61b8717b9df5b4c48b.tar.gz vz215_np1915-702cbeec081f884d336c5b61b8717b9df5b4c48b.tar.bz2 vz215_np1915-702cbeec081f884d336c5b61b8717b9df5b4c48b.zip | |
Remove index
| -rwxr-xr-x | report/metadata.yaml | 15 |
1 files changed, 9 insertions, 6 deletions
diff --git a/report/metadata.yaml b/report/metadata.yaml index d443c7f..30fd7fa 100755 --- a/report/metadata.yaml +++ b/report/metadata.yaml @@ -5,17 +5,20 @@ author: affilation: Imperial College location: London, UK email: vz215@ic.ac.uk, np@ic.ac.uk -keywords: - - one - - two - - three numbersections: yes lang: en babel-lang: english abstract: | - This is the abstract for the pattern recognition courswork. + In this coursework we will analyze the benefits of different face recognition methods. + On one hand we will analyze PCA, Principal Components Analysis. This method + allows dimensionality reduction, obtaining a generative subspace which is very reliable for + face reconstruction. - It consists of two paragraphs. + On the other hand LDA, Linear Discriminant Analysis, allows to perform a very reliable classification, + generating a discriminative subspace, in which the separation between classes is easier to recognize. + + In the final part we will analyze the benefits of using a combined version of the two methods using FIsherfaces. + As we will see, the PCA-LDA ensemble will obtain much more accurate results with a very high speed of computation. ... |