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Applied Optics

Applied Optics

  • Vol. 44, Iss. 18 — Jun. 20, 2005
  • pp: 3784–3794

Optics InfoBase > Applied Optics > Volume 44 > Issue 18 > Page 3784

Face recognition by using feature-specific imaging

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

Applied Optics, Vol. 44, Issue 18, pp. 3784-3794        doi:10.1364/AO.44.003784

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  • OCIS Codes:
  • (100.3010) Image processing : Image reconstruction techniques
  • (100.5010) Image processing : Pattern recognition
  • (110.0110) Imaging systems : Imaging systems
  • (110.6980) Imaging systems : Transforms
  • (200.4740) Optics in computing : Optical processing

Citation
Himadri S. Pal, Dinesh Ganotra, and Mark A. Neifeld, "Face recognition by using feature-specific imaging," Appl. Opt. 44, 3784-3794 (2005)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-44-18-3784

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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.

© 2005 Optical Society of America

» View Full Text: Acrobat PDF (341 KB)

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Author Affiliations

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

University of Arizona

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