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.
翻译:摄像头和个人装置的大规模可用性导致成像条件之间差异很大,如果将图像作个人识别比较,则类内变异和性能下降幅度很大。然而,随着生物鉴别解决方案的广泛部署,通常会随着被损坏或新设计的出现而取代购置硬件,或者在不同环境中的机构或应用程序之间交换信息。此外,成像带的变化也可能发生。例如,在可见的(VW)频谱中,面孔通常是获得的,而虹膜图像则在近红外线(NIR)频谱中捕捉到。然而,如果监视相机的面部需要与遗留的 iris2 数据库作比较,则可能需要进行跨频谱比较。在这里,我们建议采用多数值方法应对跨传感器或更新设计的设计,或者在不同环境中进行信息交换。我们采用基于直线式物流回归的组合系统,其中的精密分数往往与日志相似。这样可以很容易地将光谱图像结合到近红外线(NIS2) 。我们还评估了我们从一个跨透点的Iris/Peroal总体图像的图象值比值, 相对的数值比值方法, 也比了我们从20的数值比值比值,而要从20比值比值的数值比值比值比值。