Online signature recognition and writer identification by spatial-temporal neural processing

In this paper we attempt online person identification by signature recognition and also from the natural writing of a person. The basic interest is in the novelty of the technique and the methodology utilized. Our system is based on a newly developed spatio-temporal artificial neuron (STAN), which is well adapted for the recognition of spatio-temporal patterns. This neuron has the capability to process continuous asynchronous spatio-temporal data sequences and compares them with the help of Hermitian distance. The architecture of the systems developed for both of these person identification problems is identical. It is based on three modules: preprocessing, feature detection and classification. The second and third modules are based on neural architectures, which have STANs as their neurons. The architecture and training of weights of the second module is based on a spatio-temporal adaptation of Kmeans algorithm and the third module is based on an adaptation of the RCE algorithm. The results obtained Jor both the applications are encouraging.