Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/136594
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dc.contributor.authorAng, Alexandrea Shiyingen_US
dc.date.accessioned2020-01-06T02:49:27Z-
dc.date.available2020-01-06T02:49:27Z-
dc.date.issued2019-
dc.identifier.urihttps://hdl.handle.net/10356/136594-
dc.description.abstractOf the many types of cyber-security measures today, biometrics stands out as one of the most intriguing, with great potential to be explored. The advancement of technology means that threats and the ways one can attack is increasing. Face spoofing detection is one way to overcome the attack of spoofing. However, the current methods of anti-spoofing via facial recognition utilises only the light (luminance) aspect of images, without paying much attention to the chrominance or colour part. This report therefore seeks to implement and prove the functionality of a new methodology- one that involves the heavy emphasis of chrominance, through colour texture analysis. This is done using the Michigan State University’s Mobile Face Spoofing Database (MSU MFSD) and MATLAB. The results of these experiments are then compared and concluded against its literature derivation. At the end, the potential for using Deep Learning Methodology is introduced.en_US
dc.language.isoenen_US
dc.publisherNanyang Technological Universityen_US
dc.relationA2271-182en_US
dc.subjectEngineeringen_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleDesign of anti-spoofing system for face recognitionen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorChang Chip Hongen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeBachelor of Engineering (Electrical and Electronic Engineering)en_US
dc.contributor.supervisoremailECHChang@ntu.edu.sgen_US
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Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)
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