ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
Evaluation of biometric identification in open systems
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
On the individuality of the iris biometric
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
Text-dependent writer identification for Arabic handwriting
Journal of Electrical and Computer Engineering
A Keystroke Biometric Systemfor Long-Text Input
International Journal of Information Security and Privacy
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This paper is to determine the statistical validity of individuality in handwriting based on measurement of features, quantification and statistical analysis. In classification problems such as writer, face, finger print or speaker identification, the number of classes is very large or unspecified. To establish the inherent distinctness of the classes, i.e., validate individuality, we transform the many class problem into a dichotomy by using a "distance" between two samples of the same class and those of two different classes. A measure of confidence is associated with individuality. Using ten feature distance values, we trained an artificial neural network and obtained 97% overall correctness. In this experiment, 1,000 people provided three sample handwritings.