Human Body Capacitance Based Identification System On Touch Based Mobile Devices

Mobile touch devices such as smartphones, smart wearable equipments, ATM machine touch screen and so on, changed the traditional way of human beings' life.These devices usually store the user's privacy information,such as location information,shopping preferences information, bank payment information, physical health information and so on, no longer just for telephone and communication. The presence of these privacy messages makes mobile touch devices more and more private, the device has a binding relationship with the user. At the same time, to protect the privacy of equipment on the security of information becomes more important.We divided the mobile touch devices into following two categories by the type of information to authenticated user the devices based on: The user known information such as cryptogram, password; as well as identity based on the characteristics of the biology itself, such as fingerprints, iris and so on. However, these two kinds of authentication methods are inadequate: for the first method,the information known to the user is easily forgotten, and may be stolen by a malicious attacker; the second method need expensive equipment for feature acquisition.Based on the above background, we will focus on the issue of identification on touch device: how to achieve a low cost, high precision, easy and simple identification system. At last, we found that human bio-capacitance as a new type of biometrics that can be used for user identification. This features are unique and collectible, we combine human bio-capacitance and human behavior which called human-differential-capacitor,based on these new feature we designed a new user identity system. The original data is preprocessed by our own algorithm. Then we extracted the four-dimensional vector used to describe the human body differential capacitance and used SVM algorithm to do the user authentication.This system is a high-security, low-power-consumption (?A level), high accuracy(87%) and low time consumption (1.2s)recognition system, and meets the mobile devices add-on platforms requirements.