Offline Handwritten Signatures Classification Using Wavelet Packets and Level Similarity Based Scoring Poornima G Patil #1 , Ravindra S Hegadi #2 1 Department of Computer Science and Applications 2 School Of Computational Sciences 1 Dayananda Sagar Institutions 2 Solapur University 1 Shavige Malleswara Hills,Kumaraswamy Layout,Bangalore-78,India 2 Solapur, Maharashtra-413255,India 1 poornima_g_patil@yahoo.com 2 ravindrahegadi@rediffmail.com Abstract—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. I. INTRODUCTION Handwritten signatures have been used to authenticate a person since long. They are not only an accepted form of authentication in the society for every legal purpose but they are also a non invasive method. Handwritten Signature classification using computers is a really a challenging field because the signatures of the same person have variability. There can be an increase in the variability due to age, disease or emotional state of the person. Since the signature is a very small of information which adds to the complexity of the task. Handwritten Signature classification systems are either offline or online. Offline system refers to the handwritten signatures usually scanned and stored as images in the computer system. Offline signature images do not contain any dynamic information like pressure, speed, velocity etc. Offline signature classification depends upon the static features of the signatures. Hence the classification accuracy is not high. The online system refers to the signatures being captured on a tablet or a digitizing device which can record the dynamics of the signature during the act of signing. Hence can lead to higher classification accuracy. There are three types of forgeries. A. Skilled Forgeries The skilled forgeries are most difficult to handle because expert forgers practice it for some time and then they create them. B. Casual Forgeries The signer observes the signature for a while and then puts the signature in his/her own style without any knowledge of the spelling. C. Random Forgeries This is the crudest of all forgeries. The signer uses the name of the victim in his own style to create a simple forgery called as random forgery. II. WAVELET TRANSFORM A wavelet is a waveform which lasts for a limited duration and on an average its value is zero. A wavelet has a beginning and an end in contrast to the sinusoids which can extend form minus infinity to plus infinity. Wavelets facilitate the representation and analysis of signals at more than one resolution which is called as Poornima G Patil et al. / International Journal of Engineering and Technology (IJET) ISSN : 0975-4024 Vol 5 No 1 Feb-Mar 2013 421