N-latticed bounding box-based signature pattern recognition by accumulated ASCII difference

Recently, the efficiency of user recognition method is getting more importance as growing the needs for personalized service in various human-machine interaction systems. One of the representative methods for user recognition is using the signature of user, a kind of behavior-based biometric method. But most of previous online signature recognition requires a burden of computing cost. As a result, it is not well suited for various simple applications. To overcome this drawback of previous online signature recognition, simple grid-based signature representation method was developed, named as input window method. But, the input window method has basically two limitations; (1) no consideration for signature normalization and (2) noise sensitivity from the hard decision-maker such as exact string comparator. In this paper, a novel n-latticed bounding box-based signature pattern recognition method is developed by using the accumulated ASCII difference as comparator. The experiments with 8 people and 17 lattices show that signature could be normalized properly in the view of size and noise could be handled by showing 91.7% averaged success ratio. In addition, it still has the advantage of efficiency by the simplicity of data representation since the generated signature pattern is simple 1D data by the help of n-lattice lines.