Scaling up dynamic time warping for datamining applications
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Online signature verification using a new extreme points warping technique
Pattern Recognition Letters
Exact indexing of dynamic time warping
Knowledge and Information Systems
Bio-inspired reference level assigned DTW for person identification using handwritten signatures
BioID_MultiComm'09 Proceedings of the 2009 joint COST 2101 and 2102 international conference on Biometric ID management and multimodal communication
Optimal user weighting fusion in DWT domain on-line signature verification
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
Signature verification using wavelet transform and support vector machine
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
Advanced Biometric Pen System for Recording and Analyzing Handwriting
Journal of Signal Processing Systems
Area bound dynamic time warping based fast and accurate person authentication using a biometric pen
Digital Signal Processing
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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.