FIRST: Face Identity Recognition in SmarT Bank

Abstract

The rapid development of information era has influenced to realize the notion of Smart Bank via approaches like paperless services and interactive self-service systems. Since the traditional methods of identity verification are insecure and cumbersome for supporting these services, Smart Bank has been questioned. To overcome the limitations of current identity verification, it is imperative to explore an effective recognition strategy considering the trade-off between security and customer experience, which can conveniently collect identity information and accurately distinguish people. However, few research and existing systems have been reported for an integrated solution of identity verification satisfying convenient and secure banking environment. In this paper, we propose, implement, and deploy an integrated system named FIRST (Face Identity Recognition in SmarT Bank), which is a customized platform for the identity verification via face recognition in Smart Bank. FIRST uses Gabor surface feature and Fisherfaces, which can provide accurate face recognition within acceptable training time. For information acquisition, our system employs the patch-based face quality assessment, which efficiently extracts valuable faces (i.e. front face) from video streams. Furthermore, FIRST provides a distributed environment to effectively manage recognition tasks and massive data. Since March 2015, FIRST has been successfully deployed on 1800 self-service terminals in Jiangsu Province by ABC (Agriculture Bank of China), and is under deployment by State Grid in China.