Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/78655
Full metadata record
DC FieldValueLanguage
dc.contributor.authorWeng, Weiwei
dc.date.accessioned2019-06-25T05:47:24Z
dc.date.available2019-06-25T05:47:24Z
dc.date.issued2019
dc.identifier.urihttp://hdl.handle.net/10356/78655
dc.description.abstractThe changing environment, in reality, causes chief occlusions in face recognition. In other words, uncontrolled situations in face recognition is a choke point within practical applications of face recognition. Lighting normalization in recognition preprocessing contributes significantly to enhance the accuracy of recognition systems. Tremendous varying illumination conditions need to be cut down so as to achieve more compelling recognition results. Illumination normalization is a prominent concern in the cutting-edge merchant face recognition algorithms for a long time. The divisive normalization is an established method in canonical neural computation, which was developed to deal with responses in the primary visual cortex. And the divisive normalization turned to be effective by operating in the optical system and other sensory procedures. The thesis demonstrates a method by using divisive normalization as a tool to tackle with illumination variation problem in face recognition prior to recognition. The thesis will discuss a method based on the divisive normalization model, which combines the illumination estimation of adaptive smoothing and Retinex theory framework. The algorithm based on adaptive smoothing integrating discontinuity measurements and continuous convolution to generate image illumination normalization. The results gave evidence of performance improvements with the proposed procedure. It is evaluated based on the Extended Yale Face B database.en_US
dc.format.extent75 p.en_US
dc.language.isoenen_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleExploration of illumination normalization based on divisive normalizationen_US
dc.typeThesis
dc.contributor.supervisorMao Kezhien_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeMaster of Science (Computer Control and Automation)en_US
item.fulltextWith Fulltext-
item.grantfulltextrestricted-
Appears in Collections:EEE Theses
Files in This Item:
File Description SizeFormat 
dt(amended).pdf
  Restricted Access
9.43 MBAdobe PDFView/Open

Page view(s)

267
Updated on Jul 13, 2024

Download(s)

10
Updated on Jul 13, 2024

Google ScholarTM

Check

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.