This paper describes a system which can automatically determine a stroke's owner in a collaborative environment even when strokes are drawn using the same pen, in close proximity and in close timing. It identifies user purely based on physical mannerisms of how the strokes are made. The mannerisms are tilts of the pen, pressure of the pen and the speed at which the pen moves.
Unobtrusive user differentiation can be used for security and verification, improved sketch recognition through user-modelling recognition and collaboration.
Two experiments were conducted using a drawing application created in Cocoa for Mac OS X and Cintiq which was used to collect data such as tilt and pressure of the pen. First experiment had six participants and its purpose was to determine if certain features could be used to disambiguate users and how to disambiguate using tilt and pressure. The result showed that users are fairly consistent with physical manner in which they sketch and their mannerisms are distinct from one another. Experiment two was conducted using 10 participants to determine if a classifier could be created that can accurately determine creator of a stroke from set of possible creators. The classifier used 24 features and a variety of learners like Linear classifier, Quadratic classifier. K nearest neighbor gave the best results. The influence of size of the creator set on accuracy to determine the creator was also tested. The identification rate was 97.5% and 83.5% for two and ten users, respectively.
By using additional context the accuracy can be improved.
Unobtrusive user differentiation can be used for security and verification, improved sketch recognition through user-modelling recognition and collaboration.
Two experiments were conducted using a drawing application created in Cocoa for Mac OS X and Cintiq which was used to collect data such as tilt and pressure of the pen. First experiment had six participants and its purpose was to determine if certain features could be used to disambiguate users and how to disambiguate using tilt and pressure. The result showed that users are fairly consistent with physical manner in which they sketch and their mannerisms are distinct from one another. Experiment two was conducted using 10 participants to determine if a classifier could be created that can accurately determine creator of a stroke from set of possible creators. The classifier used 24 features and a variety of learners like Linear classifier, Quadratic classifier. K nearest neighbor gave the best results. The influence of size of the creator set on accuracy to determine the creator was also tested. The identification rate was 97.5% and 83.5% for two and ten users, respectively.
By using additional context the accuracy can be improved.
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