TY - GEN
T1 - Continuous authentication of smartphone users by fusing typing, swiping, and phone movement patterns
AU - Kumar, Rajesh
AU - Phoha, Vir V.
AU - Serwadda, Abdul
N1 - Funding Information:
We thank Sujit Poudel, Diksha Shukla, and Zibo Wang for their invaluable contribution during data collection. This work was supported in part by DARPA Active Authentication contract: FA 8750-13-2-0274 and by National Science Foundation Award SaTe Number: 1527795.
Publisher Copyright:
© 2016 IEEE.
PY - 2016/12/19
Y1 - 2016/12/19
N2 - We studied the fusion of three biometric authentication modalities, namely, swiping gestures, typing patterns and the phone movement patterns observed during typing or swiping. A web browser was customized to collect the data generated from the aforementioned modalities over four to seven days in an unconstrained environment. Several features were extracted by using sliding window mechanism for each modality and analyzed by using information gain, correlation, and symmetric uncertainty. Finally, five features from windows of continuous swipes, thirty features from windows of continuously typed letters, and nine features from corresponding phone movement patterns while swiping/typing were used to build the authentication system. We evaluated the performance of each modality and their fusion over a dataset of 28 users. The feature-level fusion of swiping and the corresponding phone movement patterns achieved an authentication accuracy of 93.33%, whereas, the score-level fusion of typing behaviors and the corresponding phone movement patterns achieved an authentication accuracy of 89.31 %.
AB - We studied the fusion of three biometric authentication modalities, namely, swiping gestures, typing patterns and the phone movement patterns observed during typing or swiping. A web browser was customized to collect the data generated from the aforementioned modalities over four to seven days in an unconstrained environment. Several features were extracted by using sliding window mechanism for each modality and analyzed by using information gain, correlation, and symmetric uncertainty. Finally, five features from windows of continuous swipes, thirty features from windows of continuously typed letters, and nine features from corresponding phone movement patterns while swiping/typing were used to build the authentication system. We evaluated the performance of each modality and their fusion over a dataset of 28 users. The feature-level fusion of swiping and the corresponding phone movement patterns achieved an authentication accuracy of 93.33%, whereas, the score-level fusion of typing behaviors and the corresponding phone movement patterns achieved an authentication accuracy of 89.31 %.
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U2 - 10.1109/BTAS.2016.7791164
DO - 10.1109/BTAS.2016.7791164
M3 - Conference contribution
AN - SCOPUS:85011265958
T3 - 2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems, BTAS 2016
BT - 2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems, BTAS 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th IEEE International Conference on Biometrics Theory, Applications and Systems, BTAS 2016
Y2 - 6 September 2016 through 9 September 2016
ER -