Face Recognition using Spectral and Spatial Information
Document Type
Conference Proceeding
Publication Date
10-31-2011
Journal / Book Title
SPIE
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.
DOI
10.1117/12.892743
Montclair State University Digital Commons Citation
Robila, Stefan; Chang, Marco; and D'Amico, Nisha B., "Face Recognition using Spectral and Spatial Information" (2011). Department of Computer Science Faculty Scholarship and Creative Works. 280.
https://digitalcommons.montclair.edu/compusci-facpubs/280
Published Citation
Robila, S. A., Chang, M., & D'Amico, N. B. (2011, September). Face recognition using spectral and spatial information. In Applications of Digital Image Processing XXXIV (Vol. 8135, pp. 526-532). SPIE.