This paper aims at identifying emerging computational intelligence trends for the design and modeling of complex biometric-enabled infrastructure and systems. Biometric-enabled systems are evolving towards deep learning and deep inference using the principles of adaptive computing, - the front tides of the modern computational intelligence domain. Therefore, we focus on intelligent inference engines widely deployed in biometrics. Computational intelligence applications that cover a wide spectrum of biometric tasks using physiological and behavioral traits are chosen for illustration. We highlight the technology gaps that must be addressed in future generations of biometric systems. The reported approaches and results primarily address the researchers who work towards developing the next generation of intelligent biometric-enabled systems.
@article{arxiv.2006.11971,
title = {Emerging Biometrics: Deep Inference and Other Computational Intelligence},
author = {Svetlana Yanushkevich and Shawn Eastwood and Kenneth Lai and Vlad Shmerko},
journal= {arXiv preprint arXiv:2006.11971},
year = {2020}
}