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Demonstrable and anatomy-driven knuckle identification via crease map segmentation

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Abstract

Images of the human hand can be effectively deployed to assist with the identification of the perpetrators of serious crimes. One of the prominent and distinguishing features of the human hand is found in the skin of the finger knuckle regions, which includes creases forming complex and distinctive patterns. Exploiting knuckle skin crease patterning in the identification of perpetrators requires manual labelling from expert anthropologists, which is both laborious and time-consuming. Existing approaches for automatic knuckle recognition work in a black-box manner without explicitly revealing the causes of a match or no match. Whereas, the court-room proceedings demand a more transparent and reproducible matching procedure driven from anatomy and comparison of skin creases. Hence, development of automated algorithms to segment (trace) the knuckle creases and compare them exclusively can make the whole process demonstrable and convincing. This paper proposes an effective framework for knuckle crease identification that can directly work on full hand dorsal images to (i) localize the knuckle regions effectively, (ii) segment (trace) the knuckle creases and (iii) effectively compare knuckles through the segmented crease maps. The novel matching of knuckle creases is achieved through explicit comparison of the creases themselves and is investigated with a large public dataset to demonstrate the potential of the proposed approach.

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Data Availability

The public hand databases 11k and HD are respectively available at: https://sites.google.com/view/11khands (accessed on 21 December 2021) and http://www4.comp.polyu.edu.hk/ csajaykr/knuckleV2.htm (accessed on 21 December 2021). The proprietary HUQ database (which is specifically collected under the H-Unique project) is still under collection.

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Funding

The work in this publication is supported by funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 787768).

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Authors and Affiliations

  1. School of Technology, Pandit Deendayal Energy University, Gandhinagar, Gujarat, 382007, India

    Ritesh Vyas

  2. School of Computing and Communications, Lancaster University, Lancaster, LA1 4YW, UK

    Bryan Williams, Hossein Rahmani, Richard Boswell-Challand & Plamen Angelov

  3. University of Leicester, Leicester, LE1 7RH, UK

    Zheheng Jiang

  4. St John’s College, St Giles, Oxford, OX1 3JP, UK

    Sue Black

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  1. Ritesh Vyas
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  2. Bryan Williams
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  3. Hossein Rahmani
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  4. Richard Boswell-Challand
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  6. Plamen Angelov
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  7. Sue Black
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Contributions

Conceptualization, B.M.W. and R.V.; methodology, B.M.W. and R.V.; software, R.V.; validation, B.M.W., H.R., R.V., and Z.J.; formal analysis, R.V. and B.M.W.; investigation, R.V. and B.M.W.; resources, B.M.W. and R.V.; data curation, R.B.-C., B.M.W., and R.V.; writing-original draft preparation, R.V.; writing-review and editing, B.M.W., H.R., and S.B.; visualization, B.M.W.; supervision, B.M.W., H.R., and P.A.; project administration, B.M.W. and S.B.; and funding acquisition, S.B. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Ritesh Vyas or Bryan Williams.

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The authors declare no conflict of interest.

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The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Faculty of Health and Medicine Research Ethics Committee (FHMREC) of Lancaster University (FHMREC Reference: FHMREC18001, dated 5 October 2018). Informed consent was obtained from all subjects involved in the study.

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Vyas, R., Williams, B., Rahmani, H. et al. Demonstrable and anatomy-driven knuckle identification via crease map segmentation. SIViP 19, 318 (2025). https://doi.org/10.1007/s11760-025-03940-z

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  • DOI: https://doi.org/10.1007/s11760-025-03940-z

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