Feature extraction based DCT on dynamic signature verification

Signature is a commonly accepted biometric feature for individual identification. On-line signature has begun to prevail in the last decades for it exploits dynamic features which traditional off-line signature fails to preserve. This paper presents a review of researches on on-line signature verification during recent years and lists some of the works that provide promising results.

A survey of on-line signature verification

A new online signature verification system based on combining Mellin transform, MFCC and neural network

Many people are very accustomed to the process of signing their name and having it matched for authentication. In a signature verification system, the signatures are processed to extract features that are used for verification. These features should not be duplicable. A basic problem is intraclass variations that will greatly affect the matching scores produced. The problem of distinctiveness occurs when the expectation of signatures to vary significantly between individuals is not met. There may be a large number of similarities in the feature sets used to represent the signatures of two different individuals. The efficiency of any signature verification system depends mainly on the discrimination power and robustness of the features used in the system. This study evaluates 40 functional features of viewpoint classification error and consistency for extracting the best subset once a set of features provides maximal discrimination capability between genuine and forged signatures. A modified distance of the DTW algorithm is proposed to improve performance of the verification phase. The proposed system is evaluated on the public SVC2004 signature database. The experimental results show that first, the most discriminate and consistent features are velocity based. Second, the average EER for the proposed algorithm in comparison with the general DTW algorithm shows a 5.47% decrease. Moreover, a comparative study based on a different classifier with a skilled forgery shows that the best result has an EER of 1.73% using the Parzen window classifier.

Similarity evaluation of online signatures based on modified dynamic time warping

Signature verification techniques utilize many different characteristics of an individual. The selection of signature features is critical in determining the performance of a signature verification system. Even though it is critical to select a suitable set of features to be extracted, emphasis has to be put into selecting an appropriate classifier for the features selected. This paper evaluates 19 dynamic features viewpoint classification error and discrimination capability between genuine and forgery signatures. A modified distance of DTW algorithm is proposed to improve performance of verification phase. The proposed system is evaluated on the public SVC2004 signature database. The experimental results show that first, the most discriminate and consistent features are velocity-based. Second, average EER for proposed algorithm in comparison with the general DTW algorithm show a relative decrease 46.4%.

Authentication based on signature verification using position, velocity, acceleration and Jerk signals

Automatic signature verification has been an intense research area because of the social and legal acceptance and widespread use of written signatures. It is still a challenging issue because of "small sample size " problem as well as large intra-class variations and, when considering forgeries, small inter-class variations. In order to solve these problems, we propose a two-stage fusion method to get high accuracy. At first, an EDTW (enhanced dynamic time warping) algorithm and a normalized feature measure are proposed to build a classifier based on local features. The former enhances the separability between genuine and forgery signatures, while the latter approaches the problem as a two-class pattern recognition problem, which make it possible to use training signatures as many as possible. However, local method is time and resource consuming, so we then design another classifier based on global features using majority voting rule. We fuse the global and local method by two-sage serial strategy to build an on-line signature verification system. Experimental results on SVC2004 TASK2 show good performance of our system.

Lei Hu

Papers

On-line signature verification based on fusion of global and local information