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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: CID:01120518, CID:01113180
numbersections: yes
lang: en
babel-lang: english
abstract: |
In this coursework we will analyze the benefits of different face recognition methods.
We analyze dimensionality reduction with PCA, obtaining a generative subspace which is very reliable for face reconstruction. Furthermore, we evaluate LDA, which is able to perform reliable classification, generating a discriminative subspace, where separation of classes is easier to identify.
In the final part we analyze the benefits of using a combined version of the two methods using Fisherfaces and evaluate the benefits of ensemble learning with regards to data and feature space ranodmisation. We find that combined PCA-LDA obtains lower classification error PCA or LDA individually, while also maintaining a low computational costs, allowing us to take advantage of ensemble learning.
The dataset used includes 52 classes with 10 samples each. The number of features is 2576 (46x56).
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