DTW Based Classification of Diverse Pre-Processed Time Series Obtained from Handwritten PIN Words

Personal identity verification by means of signature handwriting dynamics is a widely researched aspect of behavioral biometrics. The Dynamic Time Warping (DTW) technique has been successfully used for accessing the similarity of time series of handwritten objects by minimizing non-linear time distortions. Generally, in DTW based classifiers, the sequences are normalized in time and amplitude domains. In the paper, different length and amplitude normalization techniques are applied on signatures and handwritten PIN word sequences and their influence on accuracy of recognition are examined. A special approach to amplitude normalization based on reference level assigned Dynamic Time Warping (DTW) technique is presented. The standard deviation values calculated from the time series are used as so called bio-reference levels to improve the performance of classification. For this, they are added to the time series of query and sample datasets prior to DTW matching. The acquisition of online data is carried out by a digital pen equipped with pressure and inclination sensors. The time series obtained from the pen during handwriting provide valuable insight into the unique characteristics of the writers. Experimental results show that with the help of proposed length and amplitude normalizations of sequences including the bio-reference levels, the computational time is reduced and false acceptance rates are decreased.