Emerging Biometrics: Deep Inference and Other Computational Intelligence

TL;DR

This paper reviews emerging trends in computational intelligence for biometric systems, emphasizing deep learning and inference engines, and discusses future technology gaps for advancing biometric-enabled infrastructure.

Contribution

It identifies current trends and gaps in computational intelligence for biometric systems, focusing on deep inference and adaptive computing principles.

Findings

Deep learning is transforming biometric inference engines.

Technology gaps include scalability and robustness challenges.

Future research directions are outlined for next-generation biometric systems.

Abstract

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.