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Proceedings Paper

Face recognition using spectral and spatial information
Author(s): Stefan A. Robila; Marco Chang; Nisha B. D'Amico
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Paper Abstract

We present a novel unsupervised method for facial recognition using hyperspectral imaging and decision fusion. In previous work we have separately investigated the use of spectra matching and image based matching. In spectra matching, face spectra are being classified based on spectral similarities. In image based matching, we investigated various approaches based on orthogonal subspaces (such as PCA and OSP). In the current work we provide an automated unsupervised method that starts by detecting the face in the image and then proceeds to performs both spectral and image based matching. The results are fused in a single classification decision. The algorithm is tested on an experimental hyperspectral image database of 17 subjects each with five different facial expressions and viewing angles. Our results show that the decision fusion leads to improvement of recognition accuracy when compared to the individual approaches as well as to recognition based on regular imaging.

Paper Details

Date Published: 24 September 2011
PDF: 7 pages
Proc. SPIE 8135, Applications of Digital Image Processing XXXIV, 81351Q (24 September 2011); doi: 10.1117/12.892743
Show Author Affiliations
Stefan A. Robila, Montclair State Univ. (United States)
Marco Chang, Montclair State Univ. (United States)
Nisha B. D'Amico, Univ. of Maryland, College Park (United States)

Published in SPIE Proceedings Vol. 8135:
Applications of Digital Image Processing XXXIV
Andrew G. Tescher, Editor(s)

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