The massive availability of cameras and personal devices results in a wide variability between imaging conditions, producing large intra-class variations and performance drop if such images are compared for person recognition. However, as biometric solutions are extensively deployed, it will be common to replace acquisition hardware as it is damaged or newer designs appear, or to exchange information between agencies or applications in heterogeneous environments. Furthermore, variations in imaging bands can also occur. For example, faces are typically acquired in the visible (VW) spectrum, while iris images are captured in the near-infrared (NIR) spectrum. However, cross-spectrum comparison may be needed if for example a face from a surveillance camera needs to be compared against a legacy iris database. Here, we propose a multialgorithmic approach to cope with cross-sensor periocular recognition. We integrate different systems using a fusion scheme based on linear logistic regression, in which fused scores tend to be log-likelihood ratios. This allows easy combination by just summing scores of available systems. We evaluate our approach in the context of the 1st Cross-Spectral Iris Periocular Competition, whose aim was to compare person recognition approaches when periocular data from VW and NIR images is matched. The proposed fusion approach achieves reductions in error rates of up to 20-30 in cross-spectral NIR-VW comparison, leading to an EER of 0.22 and a FRR of just 0.62 for FAR=0.01 , representing the best overall approach of the mentioned competition.. Experiments are also reported with a database of VW images from two different smartphones, achieving even higher relative improvements in performance. We also discuss our approach from the point of view of template size and computation times, with the most computationally heavy system playing an important role in the results.
Regarding cross-sensor recognition across different spectra, the work @cite_70 proposes to compare the ocular region cropped from VW face images against NIR iris images, since face images are usually captured in the visible range, while iris images in commercial systems are usually acquired using near-infrared illumination. They employ three different feature descriptors, namely Local Binary Patterns (LBP), Normalized Gradient Correlation (NGC), and Joint Database Sparse Representation (JDSR). Using a self-captured database, they report a cross-spectral performance of EER=23 of the three experts.
@cite_70
2081358760
We consider the problem of matching face against iris images using ocular information. In biometrics, face and iris images are typically acquired using sensors operating in visible (VIS) and near-infrared (NIR) spectra, respectively. This presents a challenging problem of matching images corresponding to different biometric modalities, imaging spectra, and spatial resolutions. We propose the usage of ocular traits that are common between face and iris images (viz., iris and ocular region) to perform matching. Iris matching is performed using a commercial software, while ocular regions are matched using three different techniques: Local Binary Patterns (LBP), Normalized Gradient Correlation (NGC), and Joint Dictionary-based Sparse Representation (JDSR). Experimental results on a database containing 1358 images of 704 subjects indicate that ocular region can provide better performance than iris biometric under a challenging cross-modality matching scenario.