Hydrodynamic signatures of stationary Marangoni-driven surfactant transport

We experimentally study steady Marangoni-driven surfactant transport on the interface of a deep water layer. Using hydrodynamic measurements, and without using any knowledge of the surfactant physico-chemical properties, we show that sodium dodecyl sulphate and Tergitol 15-S-9 introduced in low concentrations result in a flow driven by adsorbed surfactant. At higher surfactant concentration, the flow is dominated by the dissolved surfactant. Using Camphoric acid, whose properties are {\it a priori} unknown, we demonstrate this method's efficacy by showing its spreading is adsorption dominated

Offline handwritten signature identification using adaptive window positioning techniques

The paper presents to address this challenge, we have proposed the use of Adaptive Window Positioning technique which focuses on not just the meaning of the handwritten signature but also on the individuality of the writer. This innovative technique divides the handwritten signature into 13 small windows of size nxn(13x13).This size should be large enough to contain ample information about the style of the author and small enough to ensure a good identification performance.The process was tested with a GPDS data set containing 4870 signature samples from 90 different writers by comparing the robust features of the test signature with that of the user signature using an appropriate classifier. Experimental results reveal that adaptive window positioning technique proved to be the efficient and reliable method for accurate signature feature extraction for the identification of offline handwritten signatures.The contribution of this technique can be used to detect signatures signed under emotional duress.Comment: 13 pages, 9 figures, 2 tables, Offline Handwritten Signature, GPDS dataset, Verification, Identification, Adaptive window positionin

Non-english and non-latin signature verification systems: A survey

Signatures continue to be an important biometric because they remain widely used as a means of personal verification and therefore an automatic verification system is needed. Manual signature-based authentication of a large number of documents is a difficult and time consuming task. Consequently for many years, in the field of protected communication and financial applications, we have observed an explosive growth in biometric personal authentication systems that are closely connected with measurable unique physical characteristics (e.g. hand geometry, iris scan, finger prints or DNA) or behavioural features. Substantial research has been undertaken in the field of signature verification involving English signatures, but to the best of our knowledge, very few works have considered non-English signatures such as Chinese, Japanese, Arabic etc. In order to convey the state-of-the-art in the field to researchers, in this paper we present a survey of non-English and non-Latin signature verification systems

Off-line Signature Verification Based on Fusion of Grid and Global Features Using Neural Networks

Signature is widely used and developed area of research for personal verification and authentication. In this paper Off-line Signature Verification Based on Fusion of Grid and Global Features Using Neural Networks (SVFGNN) is presented. The global and grid features are fused to generate set of features for the verification of signature. The test signature is compared with data base signatures based on the set of features and match/non match of signatures is decided with the help of Neural Network. The performance analysis is conducted on random, unskilled and skilled signature forgeries along with genuine signatures. It is observed that FAR and FRR results are improved in the proposed method compared to the existing algorithm

Signature Verification through Pattern Recognition

The signature is being used as a personal authentication this fact forces the need for an automatic verification system. The verification can be done either Offline or Online based on the application that is to be prepared. The Online systems use dynamic characteristics of a signature captured at the time the signature is made. While Offline systems work on the scanned image of a signature.[4[We have worked on the Offline Verification of signatures using a set of shape based geometric characteristics. Features that are used are Baseline Slant Angle, Aspect Ratio, Normalized Area, Center of Gravity, number of edge points, number of cross points, and the Slope of the line joining the Centers of Gravity of two halves of a signature scanned image. Pre-processing of a scanned image is necessary to differentiate the signature part and to remove any spurious noise present, before extracting the features. [4] The system is initially trained using a database of signatures acquired from those individuals whose signatures have to be authenticated by the system. For each subject a average signature is obtained integrating the above features derived from a set of his/her genuine sample signatures. This average signature acts as the basis for verification against a claimed test signature. Euclidian distance in the feature space between the claimed signature and the template serves as a measure of similarity between the two. If this distance is less than a pre-defined threshold (corresponding to minimum acceptable degree of similarity), the test signature is verified to be that of the claimed subject else detected as a forgery.[4] The details of pre-processing as well as the features depicted above are described in the report along with the implementation details and simulation results.[4