Face recognition via a projective compressive sensing system

Brant M. Kaylor; Charlie J. Keith; Peter A. Roos; Randy R. Reibel

doi:10.1117/12.909056

13 February 2012 Face recognition via a projective compressive sensing system

Brant M. Kaylor, Charlie J. Keith, Peter A. Roos, Randy R. Reibel

Proceedings Volume 8254, Emerging Digital Micromirror Device Based Systems and Applications IV; 82540G (2012) https://doi.org/10.1117/12.909056
Event: SPIE MOEMS-MEMS, 2012, San Francisco, California, United States

Abstract

A projective compressive sensing system for face recognition is presented. The Fisherfaces method was utilized for classification of the face images. The system uses a digital micromirror device to project measurement vectors onto the scene and a single photodetector to collect the backscattered illumination. Experimentally, the system accuracy was 95.5% using only 32 measurements per image; this performance matches the simulation results. The total number of image pixels was 5,736 (84 × 64) resulting in a compression factor of 168 over a conventional imaging system.

Citation Download Citation

Brant M. Kaylor, Charlie J. Keith, Peter A. Roos, and Randy R. Reibel "Face recognition via a projective compressive sensing system", Proc. SPIE 8254, Emerging Digital Micromirror Device Based Systems and Applications IV, 82540G (13 February 2012); https://doi.org/10.1117/12.909056

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