Abstract
In this paper, we are interested in identifying soft biometric traits such as gender (male/female), age group (below 18/18+), handedness (left/right) and hand(s) (both/single) used from the typing pattern on touchscreen phone in order to auto profiling the users online and to improve the performance of keystroke dynamics biometric system by incorporating such soft biometric scores as extra features. Four leading machine learning methods have been applied to map the typing patterns collected from 92 users through a web-based application developed by us. Obtained results in identifying such kind of traits for a typing pattern (time interval between sequences of key press and key release of entered characters) of a pre-defined text “Kolkata” are impressive. We also show the improvement of keystroke dynamics system 10% to 17% of gain accuracy using incorporation of such kind of traits with primary biometric data. This is the modest as well as an efficient approach in keystroke dynamics user authentication system in Android platform.
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Roy, S., Roy, U., Sinha, D. (2018). Identifying Soft Biometric Traits Through Typing Pattern on Touchscreen Phone. In: Mandal, J., Sinha, D. (eds) Social Transformation – Digital Way. CSI 2018. Communications in Computer and Information Science, vol 836. Springer, Singapore. https://doi.org/10.1007/978-981-13-1343-1_46
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DOI: https://doi.org/10.1007/978-981-13-1343-1_46
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