1,720,986 research outputs found
PENTOOLS - A MATLAB toolkit for on-line pen-based data experimentation
No full textMATLAB provides a powerful environment for rapid prototyping of research methods and techniques. Across the wide range of on-line pen computing applications there exists a series of common methods for the capture, storage, manipulation and measurement of handwritten data which are used as the basis for novel research and system architecture implementation. This paper outlines a new opensource toolkit for the MATLAB programming environment containing routines and data structures to perform common functionality manipulating dasiaon-line' hand drawing and writing data captured in the form of a time series sequence. The routines provide capture device interrogation, feature extraction and data manipulation, enabling full integration within MATLAB, thereby providing an extensible platform for experimentation and research
The repeatability of signatures
No full textSignatures are the most widely used form of legally binding identification and authentication. The repeatability of a person?s signature underpins its recognition and hence usefulness in everyday authentication situations. This study aims to assess the stability of a set of common features used for analysing signatures both within a single capture session and over time (multiple sessions). Secondly, the physical characteristics of signatures which result in the most repeatable performance for each feature are also analyzed. These results have implications for biometric signature verification systems and the document forensic field in that it gives an indication as to the stability of features leading potentially to improved performance and the types of features that should be analyzed given particular characteristics of the signature under investigation
The effect of the inhibition-compensation learning scheme on n-tuple based classifier performance
No full textThe inhibition-compensation learning scheme (ICLS) has been proposed as a way of enhancing the performance of the moving window classifier. In the paper the effect of ICLS on three n-tuple based classification techniques has been investigated. Pre-segmented handwritten characters from the NIST database have been used as the pattern data. Results show that approximately 2-6% gain in classification accuracy can be achieved in the OCR task domain with no adverse effect on the classification throughput
The role of handwritten signature verification in multi-modal biometric security systems
No full textBiometric technologies provide important components in thedevelopment of systems for the regulation of on-line information access. Significant application areas exist and continue to grow for these technologies. In this invited paper, a number of key issues will be addressed concerning handwriting verification and its place within a multi-modal biometric system. We shall identify some strategies and techniques for improving the reliability of the handwritten signature as a biometric modality based on a modular approach to feature analysis. We shall provide an overview of current developments in automatic signature verification, in particular defining research strands which are likely to support the translation of robust signature verification techniques from the laboratory to the market place. A number of current research topics in automatic signature verification will be covered including issues in assembling a database of “live” signature samples, enrolment validation and management of system design complexity
A method for the synthesis of dynamic biometric signature data
No full textThis paper describes a novel method for the generation of synthetic handwritten signatures, in the form of a series of time-stamped pen data channels, for use in dynamic signature verification experimentation. The technique introduces modelled variability within the generated data based on variation that is naturally found within genuine source data. Experimentation using the SVC2004 dataset and a commercial signature verification engine shows that the synthesized data achieves comparative verification performance to the use of genuine data
A novel multi-stage approach to the detection of visuo-spatial neglect based on the analysis of figure copying tasks
No full textThis paper examines a computer-based technique for the detection of visuo-spatial neglect from the responses of a simple geometric shape copying task. Defining pass/fail criteria based on the presence of drawn components, responses can be accurately and objectively assessed. More importantly, we show that by analysing novel dynamic performance features detailing timing and constructional aspects of each response, significant performance deficits can be noted in drawings made by clinically diagnosed neglect subjects that would have been classified as 'normal' using conventional static analysis, thus improving the sensitivity of the assessment
Assessing visual inattention: study of inter-rater reliability
No full textThe Rivermead Behavioural Inattention Test is widely used for the detection of visual inattention. Differences in scoring can influence the diagnosis and treatment of patients. This study aims to investigate the inter-rater reliability between 11 assessors when marking 10 identical sets of responses from the test, and to explore inter-rater agreement in scoring individual tasks
Automatic classification of hand drawn geometric shapes using constructional sequence analysis
No full textAnalysing constructional aspects of figure completion for the diagnosis of visuo-spatial neglect
No full textVisuospatial neglect (VSN) is a condition following a stroke or head injury whereby a patient fails to respond to stimuli on one side of the visual field. A standard clinical assessment technique for analysis of VSN is a pencil-and-paper based figure drawing task. Traditional static analysis of this task involves assessing the presence of the major components of the drawing. Marking of drawings is subjective, relying on assessors' own judgement and experience, and therefore no standardisation exists between assessors. Using a computer-based test capture system, this paper establishes a standardised performance assessment for a drawing task including a series of novel dynamic performance features pertaining to the timing and constructional aspects of test performance. A case study of two patients demonstrates the ability to detect VSN from a response which would have traditionally been assessed as normal and hence improve the sensitivity of the task
Diagnosis of Visuo-Spatial Neglect using Dynamic Sequence Features from a Cancellation Task
No full textVisuo-spatial neglect is recognised as a major barrier to recovery following a stroke or head injury. A standard clinical assessment technique to assess the condition is a pencil-and-paper based cancellation task. Traditional static analysis of this task involves counting the number of targets correctly cancelled on the test sheet. Using a computer-based test capture system, this paper presents the novel application of using a series of standard pattern recognition techniques to examine the diagnostic capability of a number of dynamic features relating to the sequence in which the targets were cancelled. While none of the individual dynamic features is as sensitive to neglect as the conventional static analysis, a series of standard multi-dimensional feature analysis techniques are shown to improve the classification accuracy of the dynamic properties of task execution, and hence the sensitivity to the detection of neglect and the validity of this novel application. Combining the outcome of the dynamic sequence-based features with the conventional static analysis further improves the overall sensitivity of the two cancellation tasks included in this study. The algorithmic nature of the methodology for feature extraction objectively and consistently assesses patients, thereby improving the repeatability of the task
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