Registration is a fundamental task in image processing used to match two or more pictures taken, for example, at different times, from different sensors or from different viewpoints. Over the years, a broad range of techniques have been developed for the various types of data and problems. These techniques have been independently studied for several different applications resulting in a large body of research. This paper organizes this material by establishing the relationship between the distortions in the image and the type of registration techniques which are most suitable. Two major types of distortions are distinguished. The first type are those which are the source of misregistration, i.e., they are the cause of the misalignment between the two images. Distortions which are the source of misregistration determine the transformation class which will optimally align the two images. The transformation class in turn influences the general technique that should be taken....

Abstract—We propose a new camera-based biometric: visual signature identification. We discuss the importance of the parameterization of the signatures in order to achieve good classification results, independently of variations in the position of the camera with respect to the writing surface. We show that affine arc-length parameterization performs better than conventional time and Euclidean arc-length ones. We find that the system verification performance is better than 4 percent error on skilled forgeries and 1 percent error on random forgeries, and that its recognition performance is better than 1 percent error rate, comparable to the best camera-based biometrics. Index Terms—Systems and applications, active and real-time vision, signature verification, signature recognition, biometrics. 1

There are inevitable variations in the signature patterns written by the same person. The variations can occur in the shape or in the relative positions of the characteristic features. In this paper, two methods are proposed to track the variations. Given the set of training signing samples, the first method measures the positional variations of the one-dimensional projection profiles of the signature patterns; and the second method determines the variations in relative stroke positions in the two-dimension signature patterns. The statistics on these variations are determined from the training set. Given a signature to be verified, the positional displacements are determined and the authenticity is decided based on the statistics of the training samples. For the purpose of comparison, two existing methods proposed by other researchers were implemented and tested on the same database. Furthermore, two volunteers were recruited to perform the same verification task. Results show that the proposed system compares favorably with other methods and outperforms the volunteers.

The problem of establishing correspondence and measuring the similarity of a pair of planar curves arises in many applications in computer vision and pattern recognition. This paper presents a new method for comparing planar curves and for performing matching at sub-sampling resolution. The analysis of the algorithm as well as its structural properties are described. The performance of the new technique applied to the problem of signature verification is shown and compared with the performance of the well-known Dynamic Time Warping algorithm. 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 [14]. These interfaces have the drawback that are bulk...

Abstract. In this contribution a function-based approach to on-line signature verification is presented. An initial set of 8 time sequences is used; then first and second time derivates of each function are computed over these, so 24 time sequences are simultaneously considered. A valuable function normalization is applied as a previous stage to a continuous-density HMM-based complete signal modeling scheme of these 24 functions, so no derived statistical features are employed, fully exploiting in this manner the HMM modeling capabilities of the inherent time structure of the dynamic process. In the verification stage, scores are considered not as absolute but rather as relative values with respect to a reference population, permitting the use of a best-reference score-normalization technique. Results using MCYT_Signature subcorpus on 50 clients are presented, attaining an outstanding best figure of 0.35 % EER for skilled forgeries, when signer-dependent thresholds are considered. 1

There is considerable interest in authentication based on handwritten signature verification (HSV) because HSV is superior to many other biometric authentication techniques e.g. finger prints or retinal patterns, which are reliable but much more intrusive and expensive. This paper presents a review of dynamic HSV techniques that have been reported in the literature. The paper also discusses possible applications of HSV, lists some commercial products that are available and suggests some areas for future research. 1 1 Introduction Our society is increasingly dependent on electronic storage and transmission of information and this has created a need for electronically verifying a person 's identity. Handwritten signatures have been the normal and customary way for identity verification. Although there have been occasional disputes about the authorship of handwritten signatures (Osborn, 1929; Harrison, 1958; Hilton, 1956), verification of handwritten signatures has not been a major p...

This paper describes a novel approach for signature verification and identification in an offline environment based on a quasi-multiresolution technique using GSC (Gradient, Structural and Concavity) features for feature extraction. These features when used at the word level, instead of the character level, yield promising results with accuracies as high as 78 % and 93 % for verification and identification, respectively. This method was successfully employed in our previous theory of individuality of handwriting developed at CEDAR — based on obtaining within and between writer statistical distance distributions. In this paper, exploring signature verification and identification as offline handwriting verification and identification tasks respectively, we depict a mapping from the handwriting domain to the signature domain.

A method for the automatic verification of on-line handwritten signatures using both global and local features is described. The global and local features capture various aspects of signature shape and dynamics of signature production. We demonstrate that with the addition to the global features of a local feature based on the signature likelihood obtained from Hidden Markov Models (HMM), the performance of signature verification improves significantly. The current version of the program has 2.5% equal error rate. At the 1% false rejection (FR) point, the addition of the local information to the algorithm with only global features reduced the false acceptance (FA) rate from 13% to 5%. 1 Introduction Signature verification is a common behavioral biometric to identify human beings for purposes of establishing their authority to complete an automated transaction, gaining control of a computer, or gaining physical entry to a protected area. Signatures are particularly useful for identif...

viii 1 Introduction 1 1.1 Handwritten Character Recognition : : : : : : : : : : : : : : : : : 1 1.2 Related Work : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 4 1.2.1 Feature Extraction : : : : : : : : : : : : : : : : : : : : : : 4 1.2.2 Handwriting Recognition : : : : : : : : : : : : : : : : : : : 6 1.3 Proposed Approach : : : : : : : : : : : : : : : : : : : : : : : : : : 9 1.4 Thesis Organization : : : : : : : : : : : : : : : : : : : : : : : : : : 12 2 Recognition and Representation of Numeral Patterns 13 2.1 Recognition Based on Human Logical Understanding : : : : : : : 13 2.1.1 Local Geometric Shape Features : : : : : : : : : : : : : : : 14 2.1.2 Learning of Different Contributions Among Local Shape Features : : : : : : : : : : : : : : : : : : : : : : : : : : : : 17 2.1.3 Learning of New Variants by Feature Generation : : : : : 17 2.2 Invariance Based on Biological Visual System : : : : : : : : : : : 18 2.2.1 Log-Polar Transformation : : : : : : : : : : : : : : : : :...

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...