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This paper presents methodology for user identification on smartphone and mini-tablet using finger based gestures. In this paper, a set of four features, namely Signature Precision (SP), Finger Pressure (FP), Movement Time (MT), and Speed were extracted from each gesture of eight using dynamic time warping and Euclidean distance. The features are then used individually and combined for the purpose of user identification based on the Euclidean distance and the k-nearest neighbour classifier. We concluded that the best identification accuracy results from the combinations of FP and MT features where 78.46% and 78.33% were achieved on small smartphone and Mini-tablet respectively using a dataset of 50 users.

The Effectiveness of Dynamic Features of Finger Based Gestures on Smartphones’ Touchscreens for User Identification