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Demographic-Adapted ROC Curve for Assessing Automated Matching of Latent Fingerprints

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Abstract

Although the diagnostic ability of a binary classifier system has been effectively assessed using a receiver operating characteristic (ROC) curve, the presence of covariates can affect the discriminatory capacity. This research investigates how automated tools used in forensics introduce demographic biases and discusses performance unfairness mitigation strategies. In our previous work, we evaluated the impact of demographic differentials in automatic matching of latent fingerprints and incorporated these covariates in the ROC curve. The resulting adjusted ROC curve provided error rates that account for an individual’s demographic information, which is a better measure of the discriminatory capacity compared to the pooled ROC curve. Our ROC regression model was also able to handle continuous covariates such as age as well as discrete covariates such as gender and ethnicity. In this paper, we extend the preliminary study carried out on right index latent fingerprints to right thumb instances. We investigate: (i) until which extent demographic differential vary depending on properties specific to the finger instance (e.g., size of the fingertip); (ii) the effectiveness of the proposed demographic adjusted-ROC to handle unfairness.

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Acknowledgements

The authors thank Dr. Anil Jain at the Michigan State University for the latent fingerprints matcher. This work was funded by Award No. #2019-DU-BX-0011 awarded by the National Institute of Justice, Office of Justice Programs, US Department of Justice. The opinions, findings, and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect those of the US Department of Justice. The contribution of S. Sriram to this work was related to the extraction of match scores using the MSU tool.

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  1. Mengling He and Larry Tang contributed equally to this work.

Authors and Affiliations

  1. Center for Secure Information Systems, George Mason University, 4400 University Drive, Fairfax, VA, 22030, USA

    Emanuela Marasco & Sumanth Sriram

  2. Department of Statistics and National, University of Central Florida, 4000 Central Florida Blvd, Orlando, FL, 32816, USA

    Mengling He & Larry Tang

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  1. Emanuela Marasco
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  2. Mengling He
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Correspondence to Emanuela Marasco.

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This article is part of the topical collection “Progresses in Image Processing” guest edited by P. Nagabhushan, Peter Peer, Partha Pratim Roy and Satish Kumar Singh.

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Marasco, E., He, M., Tang, L. et al. Demographic-Adapted ROC Curve for Assessing Automated Matching of Latent Fingerprints. SN COMPUT. SCI. 3, 190 (2022). https://doi.org/10.1007/s42979-022-01080-6

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