A Survey of Biometric Recognition Using Deep Learning

Abstract

Biometrics is a technique used to define, assess, and quantify a person's physical and behavioral property. In recent history, deep learning has shown impressive progress in several places, including computer vision and natural language processing for supervised learning. Since biometrics deals with a person's traits, it mainly involves supervised learning and may exploit deep learning effectiveness in other similar fields. In this article, a survey of more than 60 promising biometric works using deep learning is provided, illustrating their strengths and potential in various applications. The paper starts with biometric basics, transfer learning in deep biometrics, an overview of convolutional neural networks, and then survey work. We address all the strategies and datasets used along with their accuracy. Further, some of the main challenges when utilizing these biometric recognition models and potential future avenues for research into this field are also addressed.