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 upto four levels. The resulting decomposed signal is further subjected to wavelet multiscale 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. For a given test signature image the principal components are extracted in the same way and the principal components at each level are compared against the mean principal components of the genuine signatures at the corresponding level and the difference is within the permissible range, then a score is assigned. The collective score obtained due to all levels is used to classify the signature as genuine or forgery. The proposed system has a FAR of 12% and a FRR of 8%. Keyword-Wavelet Packet, Principal Components, Details, Approximation, Score.