Robust Periocular Recognition By Fusing Sparse Representations of Color and Geometry Information

TL;DR

This paper introduces a robust periocular recognition method that fuses geometric and color information using a re-weighted elastic net model, demonstrating improved accuracy on a challenging dataset.

Contribution

The paper presents a novel fusion approach combining geometric and color features with a re-weighted elastic net model for biometric recognition.

Findings

Improved recognition accuracy on UBIRIS.v2 dataset

Effective fusion of geometric and color information

Robustness against low-quality data

Abstract

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.