Eysenck Personality Questionnaire - Android platform
Data collection
- Number of subjects: 98 (60 male, 38 female)
- Number of samples(swipes)/subject: 58
- Controlled Acquisition: Yes
- Age range: 19-58 (average: 26.3)
- Devices: 5 identical Nexus 7 tablets
Data collected in each touch point:
- action code {ACTION_DOWN, ACTION_MOVE, ACTION_UP}
- x, y coordinates
- acceleration measured along x, y, z axes
- pressure - the pressure exerted on the screen
- finger area - a normalized value of touch area in pixels
- timestamp
Feature extraction
- average_velocity
- acceleration_at_start
- midstroke_pressure
- midstroke_finger_area
- mean_pressure
- mean_finger_area
- meangx
- meangy
- meangz
Datasets (WEKA ARFF)
Android app for data collection (Developed by Lóránt Krausz, 3rd year Informatics student, 2014-2015)
Publications
- Margit ANTAL, László Zsolt SZABÓ (2016), Biometric Authentication Based on Touchscreen Swipe Patterns. Procedia Technology
Volume 22, 2016, Pages 862 - 869 9th International Conference Interdisciplinarity in Engineering, INTER-ENG 2015, 8-9 October 2015, Tirgu Mures, Romania.
[LINK]
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Margit ANTAL, Gyozo NEMES (2016), Gender Recognition from Mobile Biometric Data. 11th IEEE International Symposium on Applied Computational Intelligence and Informatics, May 12-14, 2016, Timisoara, Romania. SACI 2016. pp. 243-248.
[LINK]
- Margit ANTAL, László Zsolt SZABÓ, Győző NEMES (2015), Predicting user identity and personality traits from mobile sensor data.
Book chapter, Information and Software Technologies, Springer, pp. 163-173.
Proceedings of the 22nd International Conference, ICIST 2016, Druskininkai, Lithuania, October 13-15, 2016. Dregvaite, Giedre, Damasevicius, Robertas (Eds.)
[LINK]
