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Super-Resolution for High Magnification Face Images , Besma Abidi1
 

Summary: Super-Resolution for High Magnification Face Images
Yi Yao1
, Besma Abidi1
, Nathan D. Kalka2
, Natalia Schmid2
, Mongi Abidi1
The University of Tennessee, Knoxville, Tennessee, 37996
West Virginia University, Morgantown, West Virginia, 26506
besma@utk.edu
ABSTRACT
Most existing face recognition algorithms require face images with a minimum resolution. Meanwhile, the rapidly
emerging need for near-ground long range surveillance calls for a migration in face recognition from close-up distances
to long distances and accordingly from low and constant resolution to high and adjustable resolution. With limited
optical zoom capability restricted by the system hardware configuration, super-resolution (SR) provides a promising
solution with no additional hardware requirements. In this paper, a brief review of existing SR algorithms is conducted
and their capability of improving face recognition rates (FRR) for long range face images is studied. Algorithms
applicable to real-time scenarios are implemented and their performances in terms of FRR are examined using the IRIS-
LRHM face database [1]. Our experimental results show that SR followed by appropriate enhancement, such as wavelet
based processing, is able to achieve comparable FRR when equivalent optical zoom is employed.
Keywords: Super-resolution, face recognition, high magnification, wide area surveillance

  

Source: Abidi, Mongi A. - Department of Electrical and Computer Engineering, University of Tennessee

 

Collections: Computer Technologies and Information Sciences