Face recognition in low resolution video sequences using super resolution

Show simple item record

dc.contributor.advisor Yang, Shanchieh
dc.contributor.advisor Lukowiak, Marcin
dc.contributor.author Arachchige, Somi Ruwan Budhagoda
dc.date.accessioned 2008-12-12T13:18:45Z
dc.date.available 2008-12-12T13:18:45Z
dc.date.issued 2008-08
dc.identifier.uri http://hdl.handle.net/1850/7770
dc.description.abstract Human activity is a major concern in a wide variety of applications, such as video surveillance, human computer interface and face image database management. Detecting and recognizing faces is a crucial step in these applications. Furthermore, major advancements and initiatives in security applications in the past years have propelled face recognition technology into the spotlight. The performance of existing face recognition systems declines significantly if the resolution of the face image falls below a certain level. This is especially critical in surveillance imagery where often, due to many reasons, only low-resolution video of faces is available. If these low-resolution images are passed to a face recognition system, the performance is usually unacceptable. Hence, resolution plays a key role in face recognition systems. In this thesis, we address this issue by using super-resolution techniques as a middle step, where multiple low resolution face image frames are used to obtain a high-resolution face image for improved recognition rates. Two different techniques based on frequency and spatial domains were utilized in super resolution image enhancement. In this thesis, we apply super resolution to both images and video utilizing these techniques and we employ principal component analysis for face matching, which is both computationally efficient and accurate. The result is a system hat can accurately recognize faces using multiple low resolution images/frames. en_US
dc.language.iso en_US en_US
dc.subject Face image en_US
dc.subject Face recognition en_US
dc.subject Image frames en_US
dc.subject Low resolution en_US
dc.subject Video surveillance en_US
dc.subject.lcc TA1650 .A73 2008
dc.subject.lcsh Human face recognition (Computer science) en_US
dc.subject.lcsh Resolution (Optics) en_US
dc.subject.lcsh Principal components analysis en_US
dc.subject.lcsh Computer vision en_US
dc.title Face recognition in low resolution video sequences using super resolution en_US
dc.type Thesis en_US
dc.description.college Kate Gleason College of Engineering en_US
dc.description.department Department of Computer Engineering en_US
dc.contributor.advisorChair Savakis, Andreas

Files in this item

Files Size Format View
SArachchigeThesis08-2008.pdf 1.953Mb PDF View/Open

This item appears in the following Collection(s)

Show simple item record

Search RIT DML


Advanced Search

Browse