Biometric-rich Gestures: A Novel Approach to Authentication on Multi-touch Devices. Bae, N., S., Ahmed, K., Isbister, K., & Memon, N. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI), pages 977-986, 2012. ACM.
Biometric-rich Gestures: A Novel Approach to Authentication on Multi-touch Devices [link]Website  abstract   bibtex   
In this paper, we present a novel multi-touch gesture-based authentication technique. We take advantage of the multi-touch surface to combine biometric techniques with gestural input. We defined a comprehensive set of five-finger touch gestures, based upon classifying movement characteristics of the center of the palm and fingertips, and tested them in a user study combining biometric data collection with usability questions. Using pattern recognition techniques, we built a classifier to recognize unique biometric gesture characteristics of an individual. We achieved a 90% accuracy rate with single gestures, and saw significant improvement when multiple gestures were performed in sequence. We found user ratings of a gestures desirable characteristics (ease, pleasure, excitement) correlated with a gestures actual biometric recognition rate - that is to say, user ratings aligned well with gestural security, in contrast to typical text-based passwords. Based on these results, we conclude that multi-touch gestures show great promise as an authentication mechanism.
@inProceedings{
 title = {Biometric-rich Gestures: A Novel Approach to Authentication on Multi-touch Devices},
 type = {inProceedings},
 year = {2012},
 identifiers = {[object Object]},
 keywords = {authentication,biometric,gesture,touchscreen},
 pages = {977-986},
 websites = {http://dx.doi.org/10.1145/2207676.2208543},
 publisher = {ACM},
 id = {eae621ec-0025-307e-8eff-817e1ff65bc7},
 created = {2018-07-12T21:31:57.882Z},
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 last_modified = {2018-07-12T21:31:57.882Z},
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 abstract = {In this paper, we present a novel multi-touch gesture-based authentication technique. We take advantage of the multi-touch surface to combine biometric techniques with gestural input. We defined a comprehensive set of five-finger touch gestures, based upon classifying movement characteristics of the center of the palm and fingertips, and tested them in a user study combining biometric data collection with usability questions. Using pattern recognition techniques, we built a classifier to recognize unique biometric gesture characteristics of an individual. We achieved a 90% accuracy rate with single gestures, and saw significant improvement when multiple gestures were performed in sequence. We found user ratings of a gestures desirable characteristics (ease, pleasure, excitement) correlated with a gestures actual biometric recognition rate - that is to say, user ratings aligned well with gestural security, in contrast to typical text-based passwords. Based on these results, we conclude that multi-touch gestures show great promise as an authentication mechanism.},
 bibtype = {inProceedings},
 author = {Bae, Napa S and Ahmed, Kowsar and Isbister, Katherine and Memon, Nasir},
 booktitle = {Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI)}
}

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