Research On Identity Authentication Technology Based On Behavioral And Physiological Characteristic

The rapid development of the Internet of Things and artificial intelligence technology has brought great convenience to people accompanied by the arrival of the information age,which has brought great convenience to people,however,its security has gradually become a serious problem which requires us great attention.The damage caused by the device's illegal control threat is the most serious,it is also a common threat faced by current smart devices.The main solution is to use the identity authentication technology to achieve security control of smart devices.The current widely used sensor-based identity authentication technologies mainly include fingerprint recognition,face recognition and gait recognition.These technologies can effectively extract some human features to achieve identity authentication,but there are also some security vulnerabilities and application scenarios constraints.Considering the shortcomings of the technology of identity authentication based on single human feature,this paper combines the physiological and behavioral feature parameters to make the extracted human feature information more multi-dimensional,and uses machine learning classifiers to achieve higher accuracy of recognition and classification.Compared with the authentication method based on single feature,the authentication method based on multi-dimensional human body features not only improves the accuracy of authentication,but also achieves the authentication under various motion states.This paper mainly does the following three aspects:First,based on the existing sensor data acquisition platform,we integrate the acceleration sensor,angular velocity sensor and PPG sensor into the wristband device.The whole acquisition device is small in size and relatively light,and the data collection work is completed by using the wristband device which provides data support for subsequent experimental analysis.Second,we analyze and compare the different types of machine learning in the classifier models,which include common classifiers like decision tree,SVM,etc.,as well as common deep learning algorithms like Convolution neural network and one-class classifiers.In addition,we optimized the structure and parameters of various classifiers using correct validation mode.Finally,we designed and implemented a high precision classifier model.Third,we designed a complete framework for the entire identity authentication system,which includes the following steps: sample feature extraction,motion state classifier,identity authentication classifier and authentication results.Finally,the entire identity authentication system which based on multi-dimensional features has a continuous,slightly sensible characteristic.