Demonstrable and Anatomy-driven Knuckle Identification via Crease Map Segmentation

Ritesh Vyas¹, Bryan M. Williams², Hossein Rahmani², Richard Boswell-Challand², Zheheng Jiang³, Plamen Angelov² and Sue Black⁴

¹ School of Technology, Pandit Deendayal Energy University, Gandhinagar, Gujarat, 382007, India
² School of Computing and Communications, Lancaster University, Lancaster, LA1 4YW, UK
³ University of Leicester, Leicester, LE1 7RH, UK
⁴ St John’s College, St Giles, Oxford, OX1 3JP, UK

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.

Citation

Vyas R, Williams B, Rahmani H, Boswell-Challand R, Jiang Z, Angelov P, Black S. Demonstrable and anatomy-driven knuckle identification via crease map segmentation. Signal, Image and Video Processing. 2025 Apr;19(4):1-1.

@article{vyas2025demonstrable,
  title={Demonstrable and anatomy-driven knuckle identification via crease map segmentation},
  author={Vyas, Ritesh and Williams, Bryan and Rahmani, Hossein and Boswell-Challand, Richard and Jiang, Zheheng and Angelov, Plamen and Black, Sue},
  journal={Signal, Image and Video Processing},
  volume={19},
  number={4},
  pages={1--11},
  year={2025},
  publisher={Springer}
}