sesa 17(11): e3

Research Article

Analysis of Targeted Mouse Movements for Gender Classification

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  • @ARTICLE{10.4108/eai.7-12-2017.153395,
        author={Nicolas Van Balen and Christopher  Ball and Haining Wang},
        title={Analysis of Targeted Mouse Movements for Gender Classification},
        journal={EAI Endorsed Transactions on Security and Safety},
        keywords={User Authentication, Behavioral Biometrics, Mouse Dynamics},
  • Nicolas Van Balen
    Christopher Ball
    Haining Wang
    Year: 2017
    Analysis of Targeted Mouse Movements for Gender Classification
    DOI: 10.4108/eai.7-12-2017.153395
Nicolas Van Balen1, Christopher Ball1, Haining Wang2,*
  • 1: College of William and Mary, Williamsburg, VA 23185, USA
  • 2: University of Delaware, Newark, DE 19716, USA
*Contact email:


Gender is one of the essential characteristics of personal identity that is often misused by online impostors for malicious purposes. This paper proposes a naturalistic approach for identity protection with a specific focus on using mouse biometrics to ensure accurate gender identification. Our underpinning rationale lies in the fact that 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. Although some research has been done on classifying user by gender using biometrics, to the best of our knowledge, no research has provided a comprehensive list of which metrics (features) of movements are actually relevant to gender classification, or method by which these metrics may be chosen. This can lead to researchers making unguided decisions on which metrics to extract from the data, doing so for convenience or personal preference. Making choices this way can lead to negatively affecting the accuracy of the model by the inclusion of metrics with little relevance to the problem, and excluding metrics of high relevance. In this paper, we outline a method for choosing metrics based on empirical evidence of natural differences in the genders, and make recommendations on the choice of metrics. The efficacy of our method is then tested through the use of a logistic regression model.