Discriminative Micropattern Exemplars for Fine-Grained Biometric Recognition

The uniqueness of many biometric modalities can be attributed to the fine-grained and randomly textured patterns that are revealed in respective normalized images. This paper introduces a new approach to accurately match such biometric images using micropattern exemplars. The images acquired using different sensors, or spectrum, from the same biometric surface often reveal localized fine-grained micropatterns that maintain high similarity in such differently sensed images. Therefore, selecting appropriate micropattern exemplars that can encode similar micropatterns in both images is expected to enhance cross-sensor matching capabilities. We introduce specialized masks that are designed to efficiently encode such microstructural patterns for more accurate biometric identification. Our experiments specifically focus on the cross-modal palm patterns and introduce micropattern exemplars to match such biometric features more accurately. The experimental results presented on multiple public and fine-grained biometrics databases validate the effectiveness of the proposed approach in characterizing micro patterns that are retained in the images from different sensors. These results are highly encouraging and validate the effectiveness of the proposed approach for cross-sensor and cross-spectral biometrics identification. Our micropattern exemplars-based approach is quite generalized, and we present reproducible experiments on other biometric databases, from iris and knuckle, to underline its potential for a range of other biometric modalities.