In the context of constantly evolving carry-on technology and its increasing accessibility, namely smart-phones and tablets, a greater need for reliable authentication means comes into sight. The current study offers an alternative solution of uninterrupted testing towards verifying user legitimacy. A continuously collected dataset of 41 users’ touch-screen inputs provides a good starting point into modeling each user’s behavior and later differentiate among users. We introduce a system capable of processing features based on raw data extracted from user-screen interactions and attempting to assign each gesture to its originator. Achieving an accuracy of over 83 %, we prove that this type of authentication system is feasible and that it can be further integrated as a continuous way of disclosing intruders within given mobile applications.
With the development of authentication technology, fingerprint and speech authentication have applied to most smart devices, which means we are stepping into the era of biometric-based authentication. As the stable biological feature, the gait is used to establish the authentication model in many researches. Most of these researches are based on extracting cycles or statistics from the gait data which used as features in the authentication process with simple machine learning algorithm. The approach presented in the paper that extracts frequency-series features from gait data from acceleration sensor and uses Weighted Support Vector Machine to recognize users. Further, this paper uses the same methodology to perform the experiment, which shows improved performance of 3.5% EER (Equal Error Rate).
Berzelius failed to make use of Faraday's electrochemical laws in his laborious determination of equivalent weights.