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

Examensarbete för masterexamen

Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12380/191815
Download file(s):
File Description SizeFormat 
191815.pdfFulltext2.08 MBAdobe PDFView/Open
Type: Examensarbete för masterexamen
Master Thesis
Title: Robust Face Recognition on Adverse 3D Data - Attaining Expression & Occlusion Invariance Using Machine Learning
Authors: Kågebäck, Mikael
Abstract: The 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%.
Keywords: Teknisk fysik;Hållbar utveckling;Informations- och kommunikationsteknik;Transport;Engineering physics;Sustainable Development;Information & Communication Technology;Transport
Issue Date: 2013
Publisher: Chalmers tekniska högskola / Institutionen för tillämpad mekanik
Chalmers University of Technology / Department of Applied Mechanics
Series/Report no.: Diploma work - Department of Applied Mechanics, Chalmers University of Technology, Göteborg, Sweden : 2013:41
URI: https://hdl.handle.net/20.500.12380/191815
Collection:Examensarbeten för masterexamen // Master Theses



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.