Handwritten signature verification using weighted fractional distance classification

Signatures are one of the behavioural biometric traits, which are widely used as a means of personal verification. Therefore, they require efficient and accurate methods of authenticating users. The use of a single distance-based classification technique normally results in a lower accuracy compared to supervised learning techniques. This paper investigates the use of a combination of multiple distance-based classification techniques, namely individually optimized re-sampling, weighted Euclidean distance, fractional distance and weighted fractional distance. Results are compared to a similar system that uses support vector machines. It is shown that competitive levels of accuracy can be obtained using distance-based classification. The best accuracy obtained is 89.2%.