Piotr Porwik

The Compact Three Stages Method of the Signature Recognition

In this paper the off-line type signature analysis have been considered. Signature image by means of three different approaches is analysed, what allows to define features (weights) of the signature. Different influences of such features were tested and stated. In this paper, personal signature is pre-processed and in the three stages signature is processed. In proposed approach the Hough transform is introduced, centre of signature gravity is determined, and the horizontal and vertical signature histograms are performed. Proposed approach gives good signature recognition level, hence described method can be used in many areas, for example in biometric authentication, as biometric computer protection or as method of the analysis of persons behaviour changes.

Dynamic signature recognition based on velocity changes of some features

Dynamic signature analysis allows us to register individuals and their hidden human behaviour. This paper presents a stroke-based approach to dynamic analysis of signature. Individual features can be identified by finding the discrete signature points like x,y-coordinates, pressure, time and pen velocity. Between signatures, the correlation measure is determined. The dynamic features are extracted from authentic and forged signatures. Experimental results show that measurement of dynamic features (velocity changes) contains important information and offers a high level of accuracy for signature verification in comparison with the results without such measurements, which will be explained in the following parts of the paper.

Some Handwritten Signature Parameters in Biometric Recognition Process

In this paper there is the off-line type signature analysis profoundly considered. The analysis consists of three stages which allow to define the features (weights) of the signature. Different influences of such features are tested and stated. In this paper personal signature is first pre-processed and then processed in the three-stage method. In proposed approach the Hough transform is introduced, the centre of signature gravity is determined, and the horizontal and vertical signature histograms are performed. Proposed approach gives good signature recognition level, hence described method can be used in many areas, for instance in biometric authentication, either as biometric computer protection or as a method of the analysis of persons behaviour changes.

A new approach to signature recognition using the fuzzy method

The paper presents a new fuzzy approach to off-line handwritten signature recognition. The solution is based on characteristic feature extraction. After finding signature’s center of gravity a number of lines are drawn through it at different angles. Cross points of generated lines and signature sample, which are further grouped and sorted, are treated as the set of features. On the basis of such structures, obtained from a chosen number of learning samples, a fuzzy model is created, called the fuzzy signature. During a verification phase the level of conformity of an input sample and the fuzzy signature is calculated. The extension in feature extraction as well as proposed fuzzy model has never been employed before. It needs to be emphasized that information stored within the verification system cannot be used to recreate the original signatures collected at the enrolment phase. The fact is particularly valuable for large databases and systems where storage safety is crucial. The solution is very flexible and allows the user to extend an intuitive structure of fuzzy sets by employing dynamic features, making the approach an on-line method. The results obtained should be still improved, similarly to the case of other known biometric systems related to signature recognition. However, the presented technique can be easily utilized in applications where FAR coefficient should be very low and is more important than FRR ratio.

The k-NN classifier and self-adaptive Hotelling data reduction technique in handwritten signatures recognition

The paper proposes a novel signature verification concept. This new approach uses appropriate similarity coefficients to evaluate the associations between the signature features. This association, called the new composed feature, enables the calculation of a new form of similarity between objects. The most important advantage of the proposed solution is case-by-case matching of similarity coefficients to a signature features, which can be utilized to assess whether a given signature is genuine or forged. The procedure, as described, has been repeated for each person presented in a signatures database. In the verification stage, a two-class classifier recognizes genuine and forged signatures. In this paper, a broad range of classifiers are evaluated. These classifiers all operate on features observed and computed during the data preparation stage. The set of signature composed features of a given person can be reduced what decrease verification error. Such a phenomenon does not occur for the raw features. The approach proposed was tested in a practical environment, with handwritten signatures used as the objects to be compared. The high level of signature recognition obtained confirms that the proposed methodology is efficient and that it can be adapted to accommodate as yet unknown features. The approach proposed can be incorporated into biometric systems.

Fuzzy approach for intrusion detection based on user's commands

The article concerns the problem of detecting masqueraders in computer systems. A masquerader in a computer system is an intruder who pretends to be a legitimate user in order to gain access to protected resources. The article presents an intrusion detection method based on a fuzzy approach. Two types of user’s activity profiles are proposed along with the corresponding data structures. The solution analyzes the activity of the computer user in a relatively short period of time, building a user’s profile. The profile is based on the most recent activity of the user, therefore, it is named the local profile. Further analysis involves creating a more general structure based on a defined number of local profiles of one user, called the fuzzy profile. It represents a generalized behavior of the computer system user. The fuzzy profiles are used directly to detect abnormalities in users’ behavior, and thus possible intrusions. The proposed solution is prepared to be able to create user’s profiles based on any countable features derived from user’s actions in computer system (i.e., used commands, mouse and keyboard data, requested network resources). The presented method was tested using one of the commonly available standard intrusion data sets containing command names executed by users of a Unix system. Therefore, the obtained results can be compared with other approaches. The results of the experiments have shown that the method presented in this article is comparable with the best intrusion detection methods, tested with the same data set, in the matter of the obtained results. The proposed solution is characterized by a very low computational complexity, which has been confirmed by experimental results.

A new signature similarity measure

The paper presents a new signature similarity measure and new efficient method of recognizing handwritten signatures. Each signature is represented as a set of features such as coordinates of signature points, pen pressure, and speed of writing. Proposed approach consists in dividing signature into windows and calculating similarity values between individual windows. The influence of the size of windows and their location in a signature has been analysed. Additionally, the influence of individual features on the signature similarity value has been examined.

A new efficient method of fingerprint image enhancement

Fingerprint images frequently have poor characteristics, because these traces are captured in crime places and are left on objects of different structure. For this reason, a lot of fingerprints have to be rejected they are useless in identification processes. Fortunately, in some cases, low quality fingerprints can be additionally analysed by means of special algorithms. In this paper, a new fingerprint enhancement method has been presented. In the proposed approach, quality of image is enhanced by means of convolution of a given image and Gaussian kernel. Finally, the local binarisation is applied. Results obtained from the experiments have demonstrated and presented the efficiency of the method.

On-Line Signature Recognition Based on an Analysis of Dynamic Feature

The paper presents a new method of signature recognition. The method consists in the division of a set containing all the points of a signature into subsets. Signature points are assigned to a given subset on the basis of an analysis of dynamic feature values registered in these points. The similarity of signatures is evaluated by determining the similarity between corresponding subsets in the signatures being compared. In the study, usefulness of individual features was also evaluated.

Handwritten Signature Recognition with Adaptive Selection of Behavioral Features

The presented work focuses on the method of handwritten signature recognition, which takes into consideration a lack of repetition of the signature features. Up till now signature recognition methods based only on signature features selection. Proposed approach allows to determine both the most useful features and methods which these features should be analyzed. In the developed method different features and similarity measures can be freely selected. Additionally, selected features and similarity measures can be different for every person.