Comparative evaluation of wavelet-based super-resolution from video for face recognition at a distance

E. Bilgazyev, S. K. Shah, I. A. Kakadiaris

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Face recognition is a challenging problem, especially when low resolution images or image sequences are used for the task. Many methods have been proposed that can combine multiple low resolution images to realize a higher resolution or super-resolved image. Nonetheless, their utility and limitations for use in face recognition are not well understood. In this paper, we present a quantitative and comparative evaluation of wavelet transform based methods for image super-resolution. We evaluate different basis functions, varying levels of decomposition, and multiple methods for coefficient fusion to maximize the benefit of the super-resolved image for the task of face recognition. We have used a Discrete Wavelet Transform and the shift-invariant Dual-Tree Complex Wavelet Transform. Results are reported across both manually generated datasets and data from a surveillance system.

Original languageEnglish (US)
Title of host publication2011 IEEE International Conference on Automatic Face and Gesture Recognition and Workshops, FG 2011
Pages559-565
Number of pages7
DOIs
StatePublished - 2011
Event2011 IEEE International Conference on Automatic Face and Gesture Recognition and Workshops, FG 2011 - Santa Barbara, CA, United States
Duration: Mar 21 2011Mar 25 2011

Publication series

Name2011 IEEE International Conference on Automatic Face and Gesture Recognition and Workshops, FG 2011

Conference

Conference2011 IEEE International Conference on Automatic Face and Gesture Recognition and Workshops, FG 2011
Country/TerritoryUnited States
CitySanta Barbara, CA
Period3/21/113/25/11

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition

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