Writer Authentication Research Articles

A survey on different feature extraction methods for writer identification and verification

Identifying and verifying a person based on scanned images of their handwriting is a needful biometric application in historical document analysis, behavioural biometrics study, forensic science, access control, graphology, and copyrights management. Writer identification and verification are still challenging in offline and online handwriting recognition. Since the performances of handwriting biometric identification and verification systems depend on both the quality and types of chosen features, this is one of the most critical phases. This article represents a literature survey on offline and online biometric features used in different scripts for writer verification and identification techniques. Several previous efficient works on online and offline writer authentication methods for biometrics using cutting-edge hand-craft features in different levels of handwriting analysis like documents, paragraphs, words, and characters are analysed systematically to date for the first time in detail.

Personal digital bodyguards for e-security, e-learning and e-health: A prospective survey

The widespread availability of hand-held devices like tablets, phablets and smart phones, along with their new handwriting digitizing and their increased computing powers, enable these to process the graphomotor dimension and the lognormal trends of human handwriting. By exploiting such capacity, it becomes possible to extend these mobile devices into Personal Digital Bodyguards (PDBs). PDBs will be able to supplement people's sensitive data protection with signature verification, equipment use security with writer authentication and handwritten CAPTCHAs processing (e-security), and to enhance human-machine interaction performances through words spotting and handwriting recognition (e-recognition). For young children, these tools will turn into interactive toys helping them to learn and master their fine motor control and become better writers. For advanced students they will enable sophisticated systems for (e-learning) and (e-testing). Moreover, PDBs will also be able to provide the user with fine motor control monitoring, which can detect stress, aging and health problems (e-health).This paper presents a prospective survey of various projects dealing with these five e-fields of investigation, focussing on state of the art results and providing directions in research and development, under the theoretical umbrella of the Kinematic Theory of human movements and its Lognormality Principle. From a practical point of view, the concept of lognormality provides a fundamental common thread, an integrative psychophysical standpoint to track the graphomotor problems of signature verification, writer identification, handwriting generation, recognition and learning.

Writer verification based on a single handwriting word samples

The writer recognition task has received a lot of interests during the last decade due to it wide range of applications. This task includes writer identification and/or writer verification. However, all the researches assumed that they dispose of a large amount of text to identify or authenticate the writer, which is never the case in real-life applications. In this paper, we present an original approach for the writer authentication task based on the analysis of a unique sample of a handwriting word. We used the Levenshtein edit distance based on Fisher-Wagner algorithm to estimate the cost of transforming one handwritten word into another. Such method has been successfully applied for signature authentication and voice recognition. In order to apply it to handwriting words, we developed a segmentation module to generate the graphemes; considered as elementary components for each word. We evaluated this approach on part of the IAM database (100 writers), where half of them provided three samples only of the same word. The obtained results are very promising since we succeed to accept correctly in 87 % of cases when we used the whole database (100 writers) and up to 92 % when we used 40 writers.

Camera-Based Biometric Writer Authentication Method Using Features Extracted from Pen Holding Style

Camera-Based Biometric Writer Authentication Method Using Features Extracted from Pen Holding Style

A Novel Approach to Dynamic Signature Verification Using Sensor-Based Data glove

Data glove is a new dimension in the field of virtual reality environments, initially designed to satisfy the stringent requirements of modern motion capture and animation professionals. In this study we try to shift the implementation of data glove from motion animation towards signature verification problem, making use of the offered multiple degrees of freedom for each finger and for the hand as well. We used an SVD-based technique to extract the feature values of different sensors' locating on corresponding fingers in the signing process and evaluated the results for writer authentication. The technique is tested with large number of authentic and forgery signatures using data gloves with 14, 5 and 4 sensor and shows a significant level of accuracy with 2.46~5.0% of EER.

The Writer Authentication by Using Syllables Frequency

An approach is depended in the recent years to distinguish any author or writer from other by analyzing his writings or essays. This is done by analyzing the syllables of writings of an author. The syllable is composed of two letters; therefore the words of the writing are fragmented to syllables and extract the most frequency syllables to become trait of that author. The research work depend on analyzed the frequency syllables in two cases, the first, when there is a space between the words, the second, when these spaces are ignored. The results is obtained from a program which scan the syllables in the text file, the performance is best in the first case since the sequence of the selected syllables is higher than the same syllables in the second case.

The Writer Authentication by Using Syllables Frequency

An approach is depended in the recent years to distinguish any author or writer from other by analyzing his writings or essays. This is done by analyzing the syllables of writings of an author. The syllable is composed of two letters; therefore the words of the writing are fragmented to syllables and extract the most frequency syllables to become trait of that author. The research work depend on analyzed the frequency syllables in two cases, the first, when there is a space between the words, the second, when these spaces are ignored. The results is obtained from a program which scan the syllables in the text file, the performance is best in the first case since the sequence of the selected syllables is higher than the same syllables in the second case.

A range/domain approximation error-based approach for fractal image compression

Fractals can be an effective approach for several applications other than image coding and transmission: database indexing, texture mapping, and even pattern recognition problems such as writer authentication. However, fractal-based algorithms are strongly asymmetric because, in spite of the linearity of the decoding phase, the coding process is much more time consuming. Many different solutions have been proposed for this problem, but there is not yet a standard for fractal coding. This paper proposes a method to reduce the complexity of the image coding phase by classifying the blocks according to an approximation error measure. It is formally shown that postponing range\slash domain comparisons with respect to a preset block, it is possible to reduce drastically the amount of operations needed to encode each range. The proposed method has been compared with three other fractal coding methods, showing under which circumstances it performs better in terms of both bit rate and/or computing time.