Adaptive pore model for fingerprint pore extraction

Qijun Zhao, Lei Zhang, Dapeng Zhang, Nan Luo, Jing Bao

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

26 Citations (Scopus)


Sweat pores have been recently employed for automated fingerprint recognition, in which the pores are usually extracted by using a computationally expensive skeletonization method or a unitary scale isotropic pore model. In this paper, however, we show that real pores are not always isotropic. To accurately and robustly extract pores, we propose an adaptive anisotropic pore model, whose parameters are adjusted adaptively according to the fingerprint ridge direction and period. The fingerprint image is partitioned into blocks and a local pore model is determined for each block. With the local pore model, a matched filter is used to extract the pores within each block. Experiments on a high resolution (1200dpi) fingerprint dataset are performed and the results demonstrate that the proposed pore model and pore extraction method can locate pores more accurately and robustly in comparison with other state-of-the-art pore extractors.
Original languageEnglish
Title of host publication2008 19th International Conference on Pattern Recognition, ICPR 2008
Publication statusPublished - 1 Dec 2008
Event2008 19th International Conference on Pattern Recognition, ICPR 2008 - Tampa, FL, United States
Duration: 8 Dec 200811 Dec 2008


Conference2008 19th International Conference on Pattern Recognition, ICPR 2008
Country/TerritoryUnited States
CityTampa, FL

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition


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