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important role in forensic document examination. However, so far there lacks a comprehensive and quantitative study on individuality of handwritten characters. Based on a large number of handwritten characters extracted from handwriting samples of 1000 individuals in US, the individuality of handwritten characters has been quantitatively measured through identification and verification models. Our study shows that in general alphabetic characters bear more individuality than numerals and use of a certain number of characters will significantly outperform the global features of handwriting samples in handwriting identification and verification. Moreover, the quantitative measurement of discriminative powers of characters offers a general guidance for selecting most-informative characters in examining forensic documents.

Signatures can be acquired with a camera-based system with enough resolution to perform verification. This paper presents the performance of a visual-acquisition signature verification system, emphasizing on the importance of the parameterization of the signature in order to achieve good classification results. A technique to overcome the lack of examples in order to estimate the generalization error of the algorithm is also described. 1 Introduction and Motivation One of the research areas that is receiving a lot of attention nowadays is the area of biometric techniques for personal identification. Signature verification belongs to this set of biometric techniques. In most systems, signature verification requires the use of electronic tablets or digitizers for on-line capturing and optical scanners for off-line conversion [17]. These interfaces have the drawback that are bulky (they need to have at least the minimum area required to sign) and complicated to use, increasing the compl...

In this paper a new method for on-line signature authentication will be presented, which is based on a event-string modelling of features derived from pen-position and pressure signals of digitizer tablets. A distance measure well known from textual pattern recognition, the Levenshtein Distance, is used for comparison of signatures and classification is carried out applying a nearest neighbor classifier. Results from a test set of 1376 signatures from 41 persons are presented, which have been conducted for four different feature sets. The results are rather encouraging, with correct identification rates of 96% at zero false classifications. 1.

Recognition of Handwritten Numerals Using Elastic Matching Patrice Scattolin Elastic matching has been used for the recognition of handwritten characters for two decades. It is usually only used for writer-dependent systems with on-line data. We attempt to use this method in a multi-writer environment for both on-line and off-line recognition of handwritten numerals. By its nature, elastic matching is best suited to single writer on-line systems. Two challenges present themselves to attain reasonable results under these conditions. First, the algorithm must be modified to better generalize the models, to recognize a wider variety of patterns with a given number of models. Secondly the off-line data is not in a suitable format as the patterns are not represented by a sequence of ordered points. We will apply two modifications to the typical elastic matching system to adapt it to the multi-writer environment and for the off-line data. To process the off-line data, we use a stroke recon...

A large number of features have been proposed by researchers for on-line signature verification. However, little work has been done in measuring the consistency and discriminative power of these features. This paper presents a comparative study of features commonly used in on-line signature verification. A consistency model is developed by generalizing the existing feature-based measure to distance-based measure. Experimental results show that the simple features like X-, Y- coordinates, the speed of writing and the angle with the X-axis are among the most consistent. Key words: On-line Signature verification, Feature selection, Consistency 1

This paper describes a prototype for Brazilian bankcheck recognition. The description is divided into three topics: bankcheck information extraction, digit amount recognition and signature verification. In bankcheck information extraction, our algorithms provide signature and digit amount images free of background pattern and bankcheck printed information. In digit amount recognition, we dealt with the digit amount segmentation and implementation of a complete numeric character recognition system involving image processing, feature extraction and neural classification. In signature verification, we designed and implemented a static signature verification system suitable for banking and commercial applications. Our signature verification algorithm is capable of detecting both simple, random and skilled forgeries. The proposed automatic bankcheck recognition prototype was intensively tested by real bankcheck data as well as simulated data providing the following performance results: for ...

Identity verification becomes a challenging task when it has to be automated with high accuracy and non-repudiability. The existing methods such as passwords and photo identity cards are inadequate to meet such heavy demands. Automated biometrics-based authentication methods can meet all the demands. An overview of the fast developing and exciting area of automated biometrics is provided in this paper. Several popular biometrics including fingerprint, face, iris are briefly described and an introduction to evaluation methods is presented. 1

. A number of vision-based biometric techniques have been proposed in the past for personal identification. We present a novel one based on visual capturing of signatures. This paper describes a system based on correlation and recursive prediction methods that can track the tip of the pen in real time, with sufficient spatio-temporal resolution and accuracy to enable signature verification. Several examples and the performance of the system are shown. 1 Introduction and Motivation A number of biometric techniques have been proposed for personal identification in the past. Among the vision-based ones, we can mention face recognition [21], [22], [23], fingerprint recognition [6], iris scanning [4] and retina scanning. Voice recognition or signature verification are the most widely known among the non-vision based ones. Signature verification requires the use of electronic tablets or digitizers for on-line capturing and optical scanners for off-line conversion [20]. These interfaces have...

Abstract. We overview biometric authentication and present a system for on-line signature verification, approaching the problem as a two-class pattern recognition problem. During enrollment, reference signatures are collected from each registered user and cross aligned to extract statistics about that user’s signature. A test signature’s authenticity is established by first aligning it with each reference signature for the claimed user. The signature is then classified as genuine or forgery, according to the alignment scores which are normalized by reference statistics, using standard pattern classification techniques. We experimented with the Bayes classifier on the original data, as well as a linear classifier used in conjunction with Principal Component Analysis (PCA). The classifier using PCA resulted in a 1.4 % error rate for a data set of 94 people and 495 signatures (genuine signatures and skilled forgeries). 1