Signature pattern recognition using moments invariant and a new fuzzy LVQ model

In this paper we have introduced a new method for signature pattern recogni tion, taking advantage of some image moment transformations combined with f uzzy logic approach. For this purpose first we tried to model the noise emb edded in signature patterns inherently and separate it from environmental e ffects. Based on the first step results, we have performed a mapping into t he unit circle using the error least mean square (LMS) error criterion, to get ride of the variations caused by shifting or scaling. Then we derived s ome orientation invariant moments introduced in former reports and studied their statistical properties in our special input space. Later we defined a fuzzy complex space and also a fuzzy complex similarity measure in this sp ace and constructed a new training algorithm based on fuzzy learning vector quantization (FLVQ) method. A comparison method has also been proposed so that any input pattern could be compared to the learned prototypes through the pre-defined fuzzy similarity measure. Each set of the above image momen ts were used by the fuzzy classifier separately and the mis-classifications were detected as a measure of error magnitude. The efficiency of the propo sed FLVQ model has been numerically shown compared to the conventional FLVQ s reported so Far. Finally some satisfactory results are derived and also a comparison is made between the above considered image transformations.