Non-Intrusive Biometric Identification for Personalized Computing Using Wireless Big Data

Human identification is of great importance for personalized and ubiquitous computing services. As the rapid and ubiquitous deployment of wireless access, the data of channel state information (CSI) gathered from wireless networks has become a useful way for recognizing human activities. In this paper, we investigate the problem of human identification based on biometrics using the wireless big data of CSI. The rationale is that human's behavioral movements cause unique impacts on wireless CSI, which can be used to recognize the corresponding biometrics and further identify different persons in a nonintrusive manner. We first propose BioID, a general framework for biometric identification with CSI. Based on the framework, we further devise a novel identification scheme based on CSI data of lip motions. By incorporating the CSI characteristics of lip motions into noise removal and feature extraction, the proposed scheme is able to identify different users. The experimental results show that the proposed scheme can achieve accurate human identification (accuracy >90%). Finally, we discuss several open issues and challenges for future direction.