In this paper, we propose a re-weighted elastic
net (REN) model for biometric recognition. The new model
is applied to data separated into geometric and color spatial
components. The geometric information is extracted using a
fast cartoon - texture decomposition model based on a dual
formulation of the total variation norm allowing us to carry
information about the overall geometry of images. Color
components are defined using linear and nonlinear color
spaces, namely the red-green-blue (RGB), chromaticitybrightness
(CB) and hue-saturation-value (HSV). Next,
according to a Bayesian fusion-scheme, sparse representations
for classification purposes are obtained. The scheme
is numerically solved using a gradient projection (GP) algorithm.
In the empirical validation of the proposed model,
we have chosen the periocular region, which is an emerging
trait known for its robustness against low quality data.
Our results were obtained in the publicly available FRGC
and UBIRIS.v2 data sets and show consistent improvements in recognition effectiveness when compared to related state-of-
author = "J. C. Moreno and V. B. S. Prasath and G. Santos and H. Proenca",
title = "Robust periocular recognition by fusing sparse representations of color and geometry information",
year = 2015,
journal = "Journal of Signal Processing Systems",
month = "Jul",
keywords = "periocular, biometrics, decomposition",
doi = "10.1007/s11265-015-1023-3",
url = "http://link.springer.com/article/10.1007%2Fs11265-015-1023-3"
J. C. Moreno, V. B. S. Prasath, G. Santos, and H. Proenca. Robust periocular recognition by fusing sparse representations of color and geometry information. Journal of Signal Processing Systems, July 2015.