dc.contributor.author | Worsey, Justin Nicholas | |
dc.date.accessioned | 2017-01-11T09:31:12Z | |
dc.date.issued | 2016-07-28 | |
dc.description.abstract | Traditional paper based attendance monitoring systems are time consuming and suscep-
tible to both error and data loss. Where technical advances have attempted to solve the
problem, they tend to improve only small portions i.e. confidence that data has been
collected satisfactorily can be very high but technology can also be difficult to use, time
consuming and impossible especially if the overall system is down. Camera based face
recognition has the potential to resolve most monitoring problems. It is passive, easy and
inexpensive to utilise; and if supported by a human safeguard can be very reliable. This
thesis evaluates a strategy to monitor lecture attendance using images captured by cheap
web cams in an unconstrained environment. A traditional recognition pipeline is utilised
in which faces are automatically detected and aligned to a standard coordinate system
before extracting Scale Invariant Feature Transform (SIFT), Local Binary Pattern (LBP)
and Eigenface based features for classification.
A greedy algorithm is employed to match captured faces to reference images with faces
labelled and added to the training set over time. Performance is evaluated on images
captured from a small lecture series over ten weeks. It is evident that performance improves
during the series as new reference material is included within the training data. This
correlation demonstrates that the success of the system is determined not only by the
on-going capturing process but also the quality and variability of the initial training data.
Whilst the system is capable of reasonable success, the experiments show that it also yields
an unacceptably high false positive rate and cannot be used in isolation. This is primarily
because the greedy nature of the algorithm allows the possibility of assigning multiple
images of the same person captured in the same lecture to different students including ‘no
shows’. | en_GB |
dc.identifier.uri | http://hdl.handle.net/10871/25155 | |
dc.language.iso | en | en_GB |
dc.publisher | University of Exeter | en_GB |
dc.subject | facial recognition face detection viola jones SIFT active shape monitoring nearest neighbour | en_GB |
dc.subject | local binary pattern | en_GB |
dc.title | Face recognition in an unconstrained environment for monitoring student attendance | en_GB |
dc.type | Thesis or dissertation | en_GB |
dc.date.available | 2017-01-11T09:31:12Z | |
dc.contributor.advisor | Everson, Richard | |
dc.publisher.department | College of Engineering, Mathematics and Physical Sciences | en_GB |
dc.type.degreetitle | MbyRes in Computer Science | en_GB |
dc.type.qualificationlevel | Masters Degree | en_GB |
dc.type.qualificationname | MbyRes | en_GB |