Signature classification by hidden Markov model

Signature recognition is a relevant area in secure applications referred to as biometric identification. The image of the signature to be recognized (in off-line systems) can be considered as a spatio-temporal signal due to the shapely geometric and sequential character of the pencil drawing. The recognition and classification methods known to us are based on the extraction of geometric parameters and their classification by either a linear or nonlinear classifier. This procedure neglects the temporal information of the signature. In order to alleviate this, this paper proposes to use signature parameters with spatio-temporal information and its classification by a classifier capable of dealing with spatio-temporal problems as hidden Markov models (HMM). The proposed parameters are calculated in two stages; first, the preprocessing stage which includes noise reduction and outline detection through a skeletonization or thinning algorithm; and second, a parameterization stage in which the signature is encoded following the signature line and recording the length and direction of the pencil drawing obtaining a vector that includes the signature spatio-temporal information. The classification of the above parameters is done by a HMM classifier working in the same way as isolated word recognition systems. To design (train and test) the HMM classifier we have built a database of 24 signatures of 60 different writers.