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---
title: 'EE4-68 Pattern Recognition (2018-2019) CW1'
author:
- name: Vasil Zlatanov, Nunzio Pucci
affilation: Imperial College
location: London, UK
email: vz215@ic.ac.uk, np1915@ic.ac.uk
numbersections: yes
lang: en
babel-lang: english
abstract: |
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.
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.
The data used includes 52 classes with 10 samples each. The number of features is 2576(since the size of the pictures is 46x56).
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