Abstract
Touch and multi-touch gestures are becoming the most common way to interact with technology such as smart phones, tablets and other mobile devices. The latest touch-screen input capacities have tremendously increased the quantity and quality of available gesture data, which has led to the exploration of its use in multiple disciplines from psychology to biometrics. Following research studies undertaken in similar modalities such as keystroke and mouse usage biometrics, the present work proposes the use of swipe gesture data for the prediction of soft-biometrics, specifically the user's sex. This paper details the software and protocol used for the data collection, the feature set extracted and subsequent machine learning analysis. Within this analysis, the BestFirst feature selection technique and classification algorithms (naïve Bayes, logistic regression, support vector machine and decision tree) have been tested. The results of this exploratory analysis have confirmed the possibility of sex prediction from the swipe gesture data, obtaining an encouraging 78% accuracy rate using swipe gesture data from two different directions. These results will hopefully encourage further research in this area, where the prediction of soft-biometrics traits from swipe gesture data can play an important role in enhancing the authentication processes based on touch-screen devices.
Highlights
Soft-biometrics traits are defined as “anatomical or behavioural characteristics that provides some information about the identity of a person, but does not provide sufficient evidence to precisely determine the identity” [1]
The score distributions for the best classifier of each direction are presented as an introduction to the last subsection, which attempts to enhance sex prediction performance by means of score and decision fusion techniques
The increasing adoption of touch-screen devices and their continuous data capture enrichment will bring the possibility of collecting high quality swipe gesture data from users interactions and the opportunity of using these data to predict soft-biometrics such sex, age category, single or-two handed usage, handedness or even emotion prediction. This soft-biometric information can be used to improve authentication systems, to tailor applications interfaces to specific user groups, or to enhance the interaction between computer-based systems and users
Summary
Soft-biometrics traits are defined as “anatomical or behavioural characteristics that provides some information about the identity of a person, but does not provide sufficient evidence to precisely determine the identity” [1]. They include characteristics such as age, ethnicity, sex, height, weight, scars and tattoos. These traits have been used within biometrics deployment in combination with hard-biometrics modalities such as fingerprint [2], iris [3] and face [4].
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