Gesture recognition using recurrent neural networks
CHI '91 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Specifying gestures by example
Proceedings of the 18th annual conference on Computer graphics and interactive techniques
Graphical input through machine recognition of sketches
SIGGRAPH '76 Proceedings of the 3rd annual conference on Computer graphics and interactive techniques
Automatic On-line Signature Verification
ACCV '98 Proceedings of the Third Asian Conference on Computer Vision-Volume I - Volume I
Improvement of On-line Signature Verification System Robust to Intersession Variability
ECCV '02 Proceedings of the International ECCV 2002 Workshop Copenhagen on Biometric Authentication
Sketch based interfaces: early processing for sketch understanding
Proceedings of the 2001 workshop on Perceptive user interfaces
"Those look similar!" issues in automating gesture design advice
Proceedings of the 2001 workshop on Perceptive user interfaces
SketchREAD: a multi-domain sketch recognition engine
Proceedings of the 17th annual ACM symposium on User interface software and technology
HMM-based efficient sketch recognition
Proceedings of the 10th international conference on Intelligent user interfaces
Local and Global Feature Selection for On-line Signature Verification
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Gestures without libraries, toolkits or training: a $1 recognizer for user interface prototypes
Proceedings of the 20th annual ACM symposium on User interface software and technology
LADDER, a sketching language for user interface developers
Computers and Graphics
Effectiveness of pen pressure, azimuth, and altitude features for online signature verification
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
Analyse procédurale: un nouveau paradigme pour l'analyse de tracés manuscrits
23rd French Speaking Conference on Human-Computer Interaction
Hi-index | 0.00 |
With the proliferation of tablet PCs and multi-touch computers, collaborative input on a single sketched surface is becoming more and more prevalent. The ability to identify which user draws a specific stroke on a shared surface is widely useful in a) security/forensics research, by effectively identifying a forgery, b) sketch recognition, by providing the ability to employ user-dependent recognition algorithms on a multi-user system, and c) multi-user collaborative systems, by effectively discriminating whose stroke is whose in a complicated diagram. To ensure an adaptive user interface, we cannot expect nor require that users will self-identify nor restrict themselves to a single pen. Instead, we prefer a system that can automatically determine a stroke's owner, even when strokes by different users are drawn with the same pen, in close proximity, and near in timing. We present the results of an experiment that shows that the creator of an individual pen strokes can be determined with high accuracy, without supra-stroke context (such as timing, pen-ID, nor location), and based solely on the physical mechanics of how these strokes are drawn (specifically, pen tilt, pressure, and speed). Results from free-form drawing data, including text and doodles, but not signature data, show that our methods differentiate a single stroke (such as that of a dot of an 'i') between two users at an accuracy of 97.5% and between ten users at an accuracy of 83.5%.