dc.contributor.advisor | Dimitrijev, Sima | |
dc.contributor.author | James, Alex Pappachen | |
dc.date.accessioned | 2018-01-23T02:29:14Z | |
dc.date.available | 2018-01-23T02:29:14Z | |
dc.date.issued | 2009 | |
dc.identifier.doi | 10.25904/1912/244 | |
dc.identifier.uri | http://hdl.handle.net/10072/366375 | |
dc.description.abstract | The human brain exhibits robustness against natural variability occurring in
face images, yet the commonly attempted algorithms for face recognition are not
modular and do not apply the principle of binary decisions made by the firing of
neurons. This thesis presents a memory based face recognition method based on
the concepts of local binary decisions and spatial change features. Local binary
decisions are inspired from the binary conversions done by firing of neurons
while spatial change features are inspired from the retinal processing of the
human visual system. Applying these principles and by using the principle of
modularity in a hierarchical manner, a class of memory based face recognition
algorithms is formed.
These algorithms when applied to difficult testing conditions show high recognition
performance. This high recognition performance is enabled by (1) local
binary decisions and (2) spatial change detection. The baseline algorithm formed
by using these two concepts is called local binary decisions on similarity (LBDS)
algorithm. An analysis is performed using the LBDS algorithm to optimize the
parameters, and to study the relative effect of spatial change features, local binary
decisions, normalization of features, normalization of similarity measure, use
of color, localization error compensation and resolution on recognition performance.
From the insights gained through the analysis, the LBDS algorithm is
further improved by incorporating various preprocessing spatial filter operations to extract more spatial information. The inclusion of preprocessing step helps
to achieve even higher recognition performance and robustness to difficult tasks.
This improved algorithm is called enhanced local binary decisions on similarity
(ELBDS) algorithm. The ELBDS algorithm is further used to incorporate the
multiple training images per person in the gallery, and is called an exemplar
based face recognition method.
The following is the overall recognition performance when using single gallery
image per person: 97% on AR, 100% on YALE, 97% on EYALE , 97% on
CALTECH, 98% on FERET(FaFb), 94% on FERET(FaFc), 74% on FERET
(FaDup1) and 76% on FERET(FaDup2). When using multiple training samples
per person, following recognition accuracies are achieved, 99.0% on AR, 99.5%
on FERET, 99.5% on ORL, 99.3% on EYALE, 100.0% on YALE and 100.0% on
CALTECH face databases. | |
dc.language | English | |
dc.publisher | Griffith University | |
dc.publisher.place | Brisbane | |
dc.rights.copyright | The author owns the copyright in this thesis, unless stated otherwise. | |
dc.subject.keywords | Local binary decisions | |
dc.subject.keywords | Face recognition | |
dc.subject.keywords | Visual cue for face recognition | |
dc.subject.keywords | Spatial filter operations | |
dc.title | A Memory Based Face Recognition Method | |
dc.type | Griffith thesis | |
gro.faculty | Science, Environment, Engineering and Technology | |
gro.rights.copyright | The author owns the copyright in this thesis, unless stated otherwise. | |
gro.hasfulltext | Full Text | |
dc.contributor.otheradvisor | Harrison, Barry | |
dc.rights.accessRights | Public | |
gro.identifier.gurtID | gu1319586856951 | |
gro.source.ADTshelfno | ADT0836 | |
gro.thesis.degreelevel | Thesis (PhD Doctorate) | |
gro.thesis.degreeprogram | Doctor of Philosophy (PhD) | |
gro.department | Griffith School of Engineering | |
gro.griffith.author | James, Alex Pappachen | |