The Application Of Weighted DTW On Handwritten Signature Verification

Handwritten signature verification is an emerging identification method based on biometrics. It can provide a more secure, reliable, and convenient new ways of identification and it is the most easily identity authentication recognized in public in a person's biological characteristics. It is also one of the current hot researches in the field of pattern recognition. Handwritten signature verification first extracts and analyzes the characteristics of signature information. Then determines the signature to be signed with the real signature is written by the same person based on the corresponding algorithm. The related technology research plays a significant role in promoting e-commerce and e-government developments and has important theoretical significance and practical value.As a handwritten signature verification method, Dynamic Time Warping has advantages of simple concept and robust algorithm. It combines time and distance measure to match global or local, compression or deformation patterns to solve the similarity measure and classification problems about dynamic model, and finds an optimal calibration path between reference mode signals R and test mode signals T. However, this method assumes that each point is equally important, so great error appears in signature verification.For these reason, we will add weight to each sample point, which will strengthen the contribution of stable points (important sampling points) to DTW, Simultaneously reducing the effects of unstable points (sub-sampling points). We mainly use acceleration, pressure and the combination of the first two as weights, by comparing the recognition rate to find an optimal feature. Experimental data indicated that pressure is the best feature in signature verification.Variance reflects the extent of the data relative to the mean. We first calculate the intra-class distance and intra-class distance for different signature using the variance formula based on the matching distance, then get the variance for different classes in writing by comparing the intra-class distance and inter-class distance, hereby we can get greater rate of increase in signature verification.In this paper, we carried out research to some extent about all the major stages of the handwritten signature verification. These stages include: Data Acquisition, Data Preprocessing, Feature Extraction and Signature Matching. In the step of Data Preprocessing, we used WACOM tablet to collect experimental data. Since the signature of the signer is arbitrary, that is, one can signature anywhere on the tablet. Meanwhile, a number of noises generated in this process. In order to improve the recognition rate we should preprocess the sample data before we verify the signature. The main method is removing noise points, putting the coordinate origin to the center of mass normalization, smoothing and so on. The feature is the only basis in the identification of the object. We should select representative and convenient feature information in our calculation. Algorithm selection has a great impact to the final result, so in the step of signature matching we introduce the advantages of weighted DTW compared with traditional DTW in detail, and conclude by comparing the experimental results: the weighted DTW has apparently improved recognition rate, and the introduction of weight features is of great significance in handwritten signature verification system.