search query: @keyword feature extraction / total: 22
reference: 5 / 22
« previous | next »
Author:Wu, Jing
Title:Online Face Recognition with Application to Proactive Augmented Reality
Publication type:Master's thesis
Publication year:2010
Pages:ix + 64      Language:   eng
Department/School:Informaatio- ja luonnontieteiden tiedekunta
Main subject:Informaatiotekniikka   (T-61)
Supervisor:Oja, Erkki
Instructor:Koskela, Markus
Electronic version URL: http://urn.fi/URN:NBN:fi:aalto-201203131473
OEVS:
Electronic archive copy is available via Aalto Thesis Database.
Instructions

Reading digital theses in the closed network of the Aalto University Harald Herlin Learning Centre

In the closed network of Learning Centre you can read digital and digitized theses not available in the open network.

The Learning Centre contact details and opening hours: https://learningcentre.aalto.fi/en/harald-herlin-learning-centre/

You can read theses on the Learning Centre customer computers, which are available on all floors.

Logging on to the customer computers

  • Aalto University staff members log on to the customer computer using the Aalto username and password.
  • Other customers log on using a shared username and password.

Opening a thesis

  • On the desktop of the customer computers, you will find an icon titled:

    Aalto Thesis Database

  • Click on the icon to search for and open the thesis you are looking for from Aaltodoc database. You can find the thesis file by clicking the link on the OEV or OEVS field.

Reading the thesis

  • You can either print the thesis or read it on the customer computer screen.
  • You cannot save the thesis file on a flash drive or email it.
  • You cannot copy text or images from the file.
  • You cannot edit the file.

Printing the thesis

  • You can print the thesis for your personal study or research use.
  • Aalto University students and staff members may print black-and-white prints on the PrintingPoint devices when using the computer with personal Aalto username and password. Color printing is possible using the printer u90203-psc3, which is located near the customer service. Color printing is subject to a charge to Aalto University students and staff members.
  • Other customers can use the printer u90203-psc3. All printing is subject to a charge to non-University members.
Location:P1 Ark Aalto  1575   | Archive
Keywords:online face recognition
feature extraction
classification
machine learning
illumination normalization
recognition accuracy
Abstract (eng): Recently, more and more researchers have concentrated on the research of video-based face recognition.
The topic of this thesis is online face recognition with application to proactive augmented reality.
We intend to solve online single-image and multiple-image face recognition problems when the influence of illumination variations is introduced.

First, three machine learning approaches are utilized in single-image face recognition: PCA-based, 2DPCA-based, and SVM-based approaches.
Illumination variations are big obstacles for face recognition.
The next step in our approach therefore involves illumination normalization.
Image preprocessing (AHE+RGIC) and invariant feature extraction (Eigenphases and LBP) methods are employed to compensate for illumination variations.
Finally, in order to improve the recognition performance, we propose several novel algorithms to multiple-image face recognition which consider the multiple images as query data for subsequent classification.
These algorithms are called MIK-NN, MMIK-NN and Kmeans+Muliple K-NN.

In conclusion, the simulation experiment results show that the LBP+x2-based method efficiently compensates for the illumination effect and MMIK-NN considerably improves the performance of online face recognition.
ED:2010-08-17
INSSI record number: 40119
+ add basket
« previous | next »
INSSI