Authentication based on feature of hand-written signature

Abstract

The typical features of the coordinate and the curvature as well as the recorded time information were analyzed in the hand-written signatures. In the hand-written signature process 10 biometric features were summarized: the amount of zero speed in direction x and direction y, the amount of zero acceleration in direction x and direction y, the total time of the hand-written signatures, the total distance of the pen traveling in the hand-written process, the frequency for lifting the pen, the time for lifting the pen, the amount of the pressure higher or lower than the threshold values. The formulae of biometric features extraction were summarized. The Gauss function was used to draw the typical information from the above-mentioned biometric features, with which to establish the hidden Markov mode and to train it. The frame of double authentication was proposed by combing the signature with the digital signature. Web service technology was applied in the system to ensure the security of data transmission. The training practice indicates that the hand-written signature verification can satisfy the needs from the office automation systems.

Keywords

behavioral biostatistics feature / hand-written signature / hidden Markov mode / signature verification