A survey of identity recognition via data fusion and feature learning

With the rapid development of the Mobile Internet and the Industrial Internet of Things, a variety of applications put forward an urgent demand for user and device identity recognition. Digital identity with hidden characteristics is essential for both individual users and physical devices. With the assistance of multimodalities as well as fusion strategies, identity recognition can be more reliable and robust. In this survey, we turn to investigate the concepts and limitations of unimodal identity recognition, the motivation, and advantages of multimodal identity recognition, and summarize the recognition technologies and applications via feature level, match score level, decision level, and rank level data fusion strategies. Additionally, we also discuss the security concerns and future research orientations of learning-based identity recognition, which enables researchers to achieve a better understanding of the current status of this field and select future research directions. This survey summarizes and expands the fusion processing technologies and methods for multi-source and multimodality data, and provides theoretical support for their applications in complicated scenarios. In addition, it enables researchers to achieve a better understanding of the current research status of this field and select proper future research directions. • User identity recognition by leveraging physiological and behavioral biometrics. • Device identity recognition by leveraging physical fingerprint. • Multi-modality data fusion strategies by combining multi-level semantic information. • Security concerns and future work towards learning-based identity recognition.