A fundamental step in biometric recognition is to identify discriminative features in order to maximize user separation. Matching systems will often require manually choosing these discriminative regions of interest for feature extraction and/or score fusion. Specifically within periocular recognition scenarios, previous works segment the eyebrow and/or eye. While such efforts demonstrate the discriminative power of these regions, in this paper we show that there are various scenarios where blindly employing this type of segmentation is not consistently effective. Thus, we introduce a novel unsupervised approach to automatically select regions in the periocular image for improved match performance. A periocular image is segmented into rectangular regions (this process is referred to as patch segmentation) which improve the overall discrimination ability of the bio-metric samples being matched. We demonstrate the efficacy of this approach via extensive numerical results on multiple periocular biometric databases exhibiting challenges commonly found in uncontrolled acquisition environments. As the proposed approach is shown to be equivalent to or better than state-of-the-art on each dataset, our results indicate that our patch segmentation is an important step which can greatly influence system performance.
['Jonathon M. Smereka', 'B. V. K. Vijaya Kumar', 'A. Rodriguez']
Selecting discriminative regions for periocular verification