Offline Handwritten Signatures Classification using Wavelet Packets and Level Similarity Based Scoring
Offline signature classification has been extensively studied for many years. The challenge in this area is the correct classification of skilled forgeries which are the result of deliberate practice to imitate the signatures of any person. In this paper, the preprocessed images of genuine handwritten signatures are subjected to analysis by wavelet packets. A regular wavelet like db4 has been used to do the decomposition up to four levels. The resulting decomposed signal is further subjected to wavelet multi-scale principal component analysis done for ten levels. The principal components are chosen according to the kais rule. The selected principal components consist of details at ten different levels and one approximation for each signature image.