1,720,993 research outputs found
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
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
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 Synthesised Word Approach to Word Retrieval in Handwritten Documents
No full textRecent technological advances have enhanced the computer-based indexing and searching of digitised printed books. The performance now achievable in this domain, however, does not at present extend to handwritten texts which inherently contain more significant letter-based variation within their content. Furthermore, in most studies that address the handwritten text retrieval problem, a large training dataset is required which, very often, influences the context and search lexicon. In this paper a novel method is described to overcome the training data problem using a character-based modelling (termed grapheme spectrum) approach and a word modelling technique (termed synthesised word) enabling the retrieval of keywords that have not explicitly been seen in the training set. When tested on an illustrative historical manuscript the performance of the proposed word retrieval technique shows a clear advantage over existing methods
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
Automatic classification of hand drawn geometric shapes using constructional sequence analysis
No full textAssessing 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
Analysing 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
Feature-based Assessment of Visuospatial Neglect Severity in a Computer-based Line Cancellation Task
No full textVisuospatial neglect is a complicated disorder that affects a large number of stroke patients and may occur in different degrees of severity. Conventional pen and paper tests used for detection of neglect rely on a battery of tests and many studies have reported that a single test is not enough for neglect detection. To give a clearer diagnosis and reduce patient testing fatigue, increasing test sensitivity is an important task. This study presents a computer-based approach to visuospatial neglect assessment whereby the importance of timing features in the detection of different degrees of neglect severity is highlighted. Results obtained from a line cancellation test showed that while static features are important for the detection of severe neglect cases, they are insufficient for the detection of some moderate and the majority of mild neglect cases among the stroke population. An in-depth static and dynamic feature assessment is carried out offering potential increase in test sensitivity
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