Robust Periocular Recognition By Fusing Sparse Representations of Color and Geometry Information
This work addresses robust biometric recognition for security applications, but it is incremental as it builds on existing sparse representation and fusion methods.
The paper tackled periocular biometric recognition by proposing a re-weighted elastic net model that fuses sparse representations of geometric and color components, achieving consistent improvements in recognition effectiveness on the UBIRIS.v2 dataset compared to state-of-the-art techniques.
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), chromaticity-brightness (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 UBIRIS.v2 data set and show consistent improvements in recognition effectiveness when compared to related state-of-the-art techniques.