  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.
