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Proceedings Paper

Assessment of super-resolution for face recognition from very-low resolution images in sensor networks
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Paper Abstract

Super-Resolution (SR) involves the registration of multiple images/frames and reconstruction of a single higher resolution image. The goal of this research is to use multiple, very-low resolution images, such as those produced from a video sequence in a wireless sensor network system, as input to the super-resolution process in a face recognition system. The algorithm used for face recognition is the Fisherfaces method with a nearest neighbor classifier used for the recognition decision. Super-resolution consists of two stages, a registration stage and a reconstruction stage. Testing images were segmented using a simple skin color detection approach. After cropping they were combined into groups of four to be used for the super-resolution algorithm using faces from three people. Each group of four images was used as an input to the Keren registration algorithm where the rotational and translation information was saved that was then entered into the robust super-resolution reconstruction algorithm to create a single high quality image, which was processed by the face recognition algorithm. An average of the same groups of four was tested as well as a centroid shifted average. Comparison was based on nearest neighbor classifier and on classification rates. The results were not in favor of the super-resolution method but instead, the centroid shifted average was the best in this study.

Paper Details

Date Published: 27 April 2009
PDF: 12 pages
Proc. SPIE 7341, Visual Information Processing XVIII, 73410I (27 April 2009); doi: 10.1117/12.820284
Show Author Affiliations
James R. Roeder, Univ. of Texas at El Paso (United States)
Sergio D. Cabrera, Univ. of Texas at El Paso (United States)


Published in SPIE Proceedings Vol. 7341:
Visual Information Processing XVIII
Zia-Ur Rahman; Stephen E. Reichenbach; Mark Allen Neifeld, Editor(s)

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