Face recognition by using feature-specific imaging

Himadri S. Pal, Dinesh Ganotra, Mark A. Neifeld

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

We present a face-recognition system based on the optical measurement of linear features. We describe a polarization-based optical system that computes linear projections of an incident irradiance distribution. We quantify the fundamental limitations of optical feature measurement. We find that higher feature fidelity can be obtained by feature-specific imaging than by postprocessing a conventional image. We present feature-fidelity results for wavelet, principal component, and Fisher features. We study face recognition by using a k-nearest neighbors classifier and two different feed-forward neural networks. Each image block is reduced to either a one- or a two-dimensional feature space for input to these recognition algorithms. As high as 99% recognition has been achieved with one-dimensional wavelet feature projections and 100% has been achieved with two-dimensional projections. A 95-fold increase in noise tolerance by use of feature-specific imaging has been demonstrated for an example of the face-recognition problem. An optical experiment is performed to validate these results.

Original languageEnglish (US)
Pages (from-to)3784-3794
Number of pages11
JournalApplied optics
Volume44
Issue number18
DOIs
StatePublished - Jun 20 2005

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics
  • Engineering (miscellaneous)
  • Electrical and Electronic Engineering

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