  Deep learning-based models have been very successful in achieving
state-of-the-art results in many of the computer vision, speech recognition,
and natural language processing tasks in the last few years. These models seem
a natural fit for handling the ever-increasing scale of biometric recognition
problems, from cellphone authentication to airport security systems. Deep
learning-based models have increasingly been leveraged to improve the accuracy
of different biometric recognition systems in recent years. In this work, we
provide a comprehensive survey of more than 120 promising works on biometric
recognition (including face, fingerprint, iris, palmprint, ear, voice,
signature, and gait recognition), which deploy deep learning models, and show
their strengths and potentials in different applications. For each biometric,
we first introduce the available datasets that are widely used in the
literature and their characteristics. We will then talk about several promising
deep learning works developed for that biometric, and show their performance on
popular public benchmarks. We will also discuss some of the main challenges
while using these models for biometric recognition, and possible future
directions to which research in this area is headed.
