Robust Face Recognition on Adverse 3D Data - Attaining Expression & Occlusion Invariance Using Machine Learning

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dc.contributor.authorKågebäck, Mikael
dc.contributor.departmentChalmers tekniska högskola / Institutionen för tillämpad mekaniksv
dc.contributor.departmentChalmers University of Technology / Department of Applied Mechanicsen
dc.date.accessioned2019-07-03T13:20:32Z
dc.date.available2019-07-03T13:20:32Z
dc.date.issued2013
dc.description.abstractThe emerging field of high resolution mobile and inexpensive depth cameras, promise to revolutionize many parts of computer vision. One area in particular where 3D data has been shown to improve performance, is face recognition. Using a combination of local and global pattern matching and a committee of neural networks, this thesis present a robust 3D face recognition approach, decisively outperforming current methods. The system is evaluated on the Bosphorus database, a challenging benchmarking dataset that include face scans with both facial expressions and partial occlusions, captured in angles of up to 90 rotation. The proposed system achieves a recognition rate of 98:9%, which is the highest recognition rate ever reported on the Bosphorus database, improving the state of the art by 5:2%.
dc.identifier.urihttps://hdl.handle.net/20.500.12380/191815
dc.language.isoeng
dc.relation.ispartofseriesDiploma work - Department of Applied Mechanics, Chalmers University of Technology, Göteborg, Sweden : 2013:41
dc.setspec.uppsokTechnology
dc.subjectTeknisk fysik
dc.subjectHållbar utveckling
dc.subjectInformations- och kommunikationsteknik
dc.subjectTransport
dc.subjectEngineering physics
dc.subjectSustainable Development
dc.subjectInformation & Communication Technology
dc.subjectTransport
dc.titleRobust Face Recognition on Adverse 3D Data - Attaining Expression & Occlusion Invariance Using Machine Learning
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster Thesisen
dc.type.uppsokH
local.programmeComplex adaptive systems (MPCAS), MSc
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