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24 September 2011Face recognition using spectral and spatial information
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.
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Stefan A. Robila, Marco Chang, Nisha B. D'Amico, "Face recognition using spectral and spatial information," Proc. SPIE 8135, Applications of Digital Image Processing XXXIV, 81351Q (24 September 2011); https://doi.org/10.1117/12.892743