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Mobile Implementation of Enhanced Dynamic Signature Verification for the Smart-phone
Abstract
We propose a new enhanced graphical user interface and algorithm for dynamic signature verification using Smart-phone. Also, we describe the performance results of our dynamic signature verification system, which determine the authentication of signatures by comparing and analyzing various dynamic data shape of the signature, writing speed, slant of shape, and the order and number of strokes for personal signatures using an electronic pen, expecting the system to be understood and utilized widely in the industrial field.
There are three ways how to extract forensic evidence from mobile phone, such as SYN, JTAG, Revolving. However, it should be a different way to extract forensic evidence due to the differences of their usage and technology between them(mobile phone and smart phone). Therefore, in this paper, I will come up with extraction method that forensics evidence by search and seizure of a smart phone. This study aims to analyze specifications and O.S., backup analysis, evidence in smart to analyze for search and seizure of a smart phone commonly used google android and windows mobile smart phone. This study also aim to extract forensics evidence related to google android and phone book, SMS, photos, video of window mobile smart phone to make legal evidence and forensics report. It is expected that this study on smart phone forensics technology will contribute to developing mobile forensics technology.
This paper is a research on the dynamic signature verification of error rate which are false rejection rate and false acceptance rate, the size of signature verification engine, the size of the characteristic vectors of a signature, the ability to distinguish similar signatures, the processing speed and so on. Also, we present our efficient user interface and performance results.
A lot of different features have been proposed for on-line signature verification. By using these features, researchers implicitly
believe they have high consistency as well as high discriminatory power. However, very little work has been done to measure
the real consistency of these features. In this paper, we propose a model for consistency measure. Experiments were conducted
to compare a comprehensive set of features commonly used for on-line signature verification.
Actually great inters to develop a robust on-line signatureverification system has been increased. In this field, threekinds of forgeries: random forgery, simple forgery andexpert forgery must consider. In this paper a dynamicfeatures extraction for an on-line signature verificationsystem is presented. The dynamic features are extracted fromauthentic and forged signatures witch relatively lowcomputational cost. In the proposed system, all kind offorgeries included expert forgeries are considered to detectas forged signatures. The computer simulation results showus a desirable performance of the proposed system.
In this paper a new method for on-line signature authen- tication will be presented, which is based on a event-string modelling of features derived from pen-position and pres- sure signals of digitizer tablets. A distance measure well known from textual pattern recognition, the Levenshtein Distance, is used for comparison of signatures and classifi- cation 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 cor- rect identification rates of 96% at zero false classifications.
Selection of Points for On-Line Signature Comparison', Ninth International Workshop on Frontiers in Handwriting Recognition
- M Wirotius
- J.-Y Ramel
- N Vincent
Recent Advancements in Automatic Signature Verification', Ninth International Workshop on Frontiers in Handwriting Recognition
- G Dimauro
- S Impedovo
- M G Lucchese
- R Modugno
- G Pirlo
ER: An Intuitive Similarity Measure for On-Line Signature Verification', Ninth International Workshop on Frontiers in Handwriting Recognition
- Hansheng Lei
- Srinivas Palla
- Venu Govindaraju