Dynamic handwritten signature and machine learning based identity verification for keyless cryptocurrency transactions

Abstract In this paper we propose a novel system for identity verification by amalgamating online signature verification, machine learning, IOT and blockchain to garner their potentials to cope up and to contain this risk of identity theft specifically in the case of online transactions. In this system signals of roll, pitch and yaw values retrieved from MPU6050 sensor (Inertial Measurement Unit) are analysed using Digital Time Wrapping to obtain DTW minimum distance to verify the identity of the user. In case of cryptocurrencies, we propose a system where private key is not stored anywhere but the same unique private key, assigned to the user by Blockchain, is generated every time with the help of method incorporating biometrics and machine learning. The required data will then be sent to blockchain with the help of IOT system to complete the transaction.

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