Signature Verification Approach using Fusion of Hybrid Texture Features

In this paper, a writer-dependent signature verification method is proposed. Two different types of texture features, namely Wavelet and Local Quantized Patterns (LQP) features, are employed to extract two kinds of transform and statistical based information from signature images. For each writer two separate one-class support vector machines (SVMs) corresponding to each set of LQP and Wavelet features are trained to obtain two different authenticity scores for a given signature. Finally, a score level classifier fusion method is used to integrate the scores obtained from the two one-class SVMs to achieve the verification score. In the proposed method only genuine signatures are used to train the one-class SVMs. The proposed signature verification method has been tested using four different publicly available datasets and the results demonstrate the generality of the proposed method. The proposed system outperforms other existing systems in the literature.Comment: Neural Computing and Applicatio

A review of clustering techniques and developments

© 2017 Elsevier B.V. This paper presents a comprehensive study on clustering: exiting methods and developments made at various times. Clustering is defined as an unsupervised learning where the objects are grouped on the basis of some similarity inherent among them. There are different methods for clustering the objects such as hierarchical, partitional, grid, density based and model based. The approaches used in these methods are discussed with their respective states of art and applicability. The measures of similarity as well as the evaluation criteria, which are the central components of clustering, are also presented in the paper. The applications of clustering in some fields like image segmentation, object and character recognition and data mining are highlighted

Fuzzy Logic Classification of Handwritten Signature Based Computer Access and File Encryption

Often times computer access and file encryption is successful based on how complex a password will be, how often users could change their complex password, the length of the complex password and how creative users are in creating a complex passsword to stand against unauthorized access to computer resources or files. This research proposes a new way of computer access and file encryption based on the fuzzy logic classification of handwritten signatures. Feature extraction of the handwritten signatures, the Fourier transformation algorithm and the k-Nearest Algorithm could be implemented to determine how close the signature is to the signature on file to grant or deny users access to computer resources and encrypted files. lternatively implementing fuzzy logic algorithms and fuzzy k-Nearest Neighbor algorithm to the captured signature could determine how close a signature is to the one on file to grant or deny access to computer resources and files. This research paper accomplishes the feature recognition firstly by extracting the features as users sign their signatures for storage, and secondly by determining the shortest distance between the signatures. On the other hand this research work accomplish the fuzzy logic recognition firstly by classifying the signature into a membership groups based on their degree of membership and secondly by determining what level of closeness the signatures are from each other. The signatures were collected from three selected input devices- the mouse, I-Pen and the IOGear. This research demonstrates which input device users found efficient and flexible to sign their respective names. The research work also demonstrates the security levels of implementing the fuzzy logic, fuzzy k-Nearest Neighbor, Fourier Transform.Master'sCollege of Arts and Sciences: Computer ScienceUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/117719/1/Kwarteng.pd

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

Drawing, Handwriting Processing Analysis: New Advances and Challenges

International audienceDrawing and handwriting are communicational skills that are fundamental in geopolitical, ideological and technological evolutions of all time. drawingand handwriting are still useful in defining innovative applications in numerous fields. In this regard, researchers have to solve new problems like those related to the manner in which drawing and handwriting become an efficient way to command various connected objects; or to validate graphomotor skills as evident and objective sources of data useful in the study of human beings, their capabilities and their limits from birth to decline

Recognizing Handwriting Styles in a Historical Scanned Document Using Unsupervised Fuzzy Clustering

The forensic attribution of the handwriting in a digitized document to multiple scribes is a challenging problem of high dimensionality. Unique handwriting styles may be dissimilar in a blend of several factors including character size, stroke width, loops, ductus, slant angles, and cursive ligatures. Previous work on labeled data with Hidden Markov models, support vector machines, and semi-supervised recurrent neural networks have provided moderate to high success. In this study, we successfully detect hand shifts in a historical manuscript through fuzzy soft clustering in combination with linear principal component analysis. This advance demonstrates the successful deployment of unsupervised methods for writer attribution of historical documents and forensic document analysis.Comment: 26 pages in total, 5 figures and 2 table