A Behavioral Biometrics Based Approach to Online Gender Classification

Gender is one of the essential characteristics of personal identity but is often misused by online impostors for malicious purposes. However, men and women differ in their natural aiming movements of a hand held object in two-dimensional space due to anthropometric, biomechanical, and perceptual-motor control differences between the genders. Exploiting these natural gender differences, this paper proposes a naturalistic approach for gender classification based on mouse biometrics. Although some previous research has been done on gender classification using behavioral biometrics, most of them focuses on keystroke dynamics and, more importantly, none of them provides a comprehensive guideline for which metrics (features) of movements are actually relevant to gender classification. In this paper, we present a method for choosing metrics based on empirical evidence of natural difference in the genders. In particular, we develop a novel gender classification model and evaluate the model’s accuracy based on the data collected from a group of 94 users. Temporal, spatial, and accuracy metrics are recorded from kinematic and spatial analyses of 256 mouse movements performed by each user. A mouse signature for each user is created using least-squares regression weights determined by the influence movement target parameters (size of the target, horizontal and vertical distances moved). The efficacy of our model is validated through the use of binary logistic regressions.