NeuroBiometric:An Eye Blink Based Biometric Authentication System Using an Event-Based Neuromorphic Vision Sensor

作     者：Guang Chen Fa Wang Xiaoding Yuan Zhijun Li Zichen Liang Alois Knoll Guang Chen;Fa Wang;Xiaoding Yuan;Zhijun Li;Zichen Liang;Alois Knoll

作者机构：the Tongji UniversityShanghai 200092Chinaand also with the Technical University of MunichMunich 80333Germany the Tongji UniversityShanghai 200092China the University of Science and Technology of ChinaHefei 230026China the Technical University of MunichMunich 80333Germany 

出 版 物：《IEEE/CAA Journal of Automatica Sinica》 (自动化学报（英文版）)

年 卷 期：2021年第8卷第1期

页      面：206-218页

核心收录：

学科分类：0810[工学-信息与通信工程] 1205[管理学-图书情报与档案管理] 08[工学] 080202[工学-机械电子工程] 0802[工学-机械工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Natural Science Foundation of China(61906138) the National Science and Technology Major Project of the Ministry of Science and Technology of China(2018AAA0102900) the Shanghai Automotive Industry Sci-Tech Development Program(1838) the European Union’s Horizon 2020 Research and Innovation Program(785907) the Shanghai AI Innovation Development Program 2018。 

主　　题：Biometrics biometric autentication event-based vision neuromorphic vision 

摘      要：The rise of the Internet and identity authentication systems has brought convenience to people s lives but has also introduced the potential risk of privacy leaks.Existing biometric authentication systems based on explicit and static features bear the risk of being attacked by mimicked data.This work proposes a highly efficient biometric authentication system based on transient eye blink signals that are precisely captured by a neuromorphic vision sensor with microsecond-level temporal resolution.The neuromorphic vision sensor only transmits the local pixel-level changes induced by the eye blinks when they occur,which leads to advantageous characteristics such as an ultra-low latency response.We first propose a set of effective biometric features describing the motion,speed,energy and frequency signal of eye blinks based on the microsecond temporal resolution of event densities.We then train the ensemble model and non-ensemble model with our Neuro Biometric dataset for biometrics authentication.The experiments show that our system is able to identify and verify the subjects with the ensemble model at an accuracy of 0.948 and with the non-ensemble model at an accuracy of 0.925.The low false positive rates(about 0.002)and the highly dynamic features are not only hard to reproduce but also avoid recording visible characteristics of a user s appearance.The proposed system sheds light on a new path towards safer authentication using neuromorphic vision sensors.