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Effects of lossy compression on face recognition algorithms

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dc.contributor Graduate Program in Electrical and Electronic Engineering.
dc.contributor.advisor Sankur, Bülent.
dc.contributor.author Kamaşak, Mustafa.
dc.date.accessioned 2023-03-16T10:22:20Z
dc.date.available 2023-03-16T10:22:20Z
dc.date.issued 1999.
dc.identifier.other EE 1999 K17
dc.identifier.uri http://digitalarchive.boun.edu.tr/handle/123456789/13068
dc.description.abstract Face databases can consist of a few hundreds of face images to thousands, even millions. Because of storage and banwidth limitations, face databases are maintained under compressed domain. One of the related problems is the performance evaluation of traditional face recognition techniques on the compressed face images. The effects of information loss due to the compression, on the performance of principal face recognition techniques, the most robust face recognition technique against compression, the extend to which face images can be compressed without a major performance deterioration and the most appropriate compression technique for face images are determined. It is concluded that the face images can be compressed to 100:1 with face-specific compression techniques, 40:1 with SPIHT technique and 20:1 with VQ, JPEG and JPEG-2000 techniques. Most robust face recognition technique against compression is "Fisherface" method. The eigenfaces generated from compressed face images at 0.4 bit/pixel rate performed better recognition than eigenfaces generated from non-compressed images for VQ, JPEG and JPEG-2000 techniques.
dc.format.extent 30 cm.
dc.publisher Thesis (M.S.)- Bogazici University. Institute for Graduate Studies in Science and Engineering, 1999.
dc.relation Includes appendices.
dc.relation Includes appendices.
dc.subject.lcsh Human face recognition (Computer science)
dc.subject.lcsh Neural networks (Computer science)
dc.subject.lcsh Pattern recognition systems.
dc.title Effects of lossy compression on face recognition algorithms
dc.format.pages xvi, 75 leaves;


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