A Survey of the Methods on Fingerprint Orientation Field Estimation
Bian, Weixin; Xu, Deqin; Li, Qingde; Cheng, Yongqiang; Jie, Biao; Ding, Xintao
Dr Qingde Li Q.Li@hull.ac.uk
Dr Yongqiang Cheng Y.Cheng@hull.ac.uk
Fingerprint orientation field (FOF) estimation plays a key role in enhancing the performance of the automated fingerprint identification system (AFIS): accurate estimation of FOF can evidently improve the performance of AFIS. However, despite the enormous attention on the FOF estimation research in the past decades, the accurate estimation of FOFs, especially for poor-quality fingerprints, still remains a challenging task. In this paper, we devote to review and categorization of the large number of FOF estimation methods proposed in the specialized literature, with particular attention to the most recent work in this area. Broadly speaking, the existing FOF estimation methods can be grouped into three categories: gradient-based methods, mathematical models-based methods, and learning-based methods. Identifying and explaining the advantages and limitations of these FOF estimation methods is of fundamental importance for fingerprint identification, because only a full understanding of the nature of these methods can shed light on the most essential issues for FOF estimation. In this paper, we make a comprehensive discussion and analysis of these methods concerning their advantages and limitations. We have also conducted experiments using publically available competition dataset to effectively compare the performance of the most relevant algorithms and methods.
|Journal Article Type||Article|
|Publisher||Institute of Electrical and Electronics Engineers|
|Peer Reviewed||Peer Reviewed|
|APA6 Citation||Bian, W., Xu, D., Li, Q., Cheng, Y., Jie, B., & Ding, X. (2019). A Survey of the Methods on Fingerprint Orientation Field Estimation. IEEE access : practical innovations, open solutions, 7, 32644-32663. https://doi.org/10.1109/access.2019.2903601|
|Keywords||General Engineering; General Materials Science; General Computer Science|
© 2019 IEEE. Translations and content mining are permitted for academic research only.
Personal use is also permitted, but republication/redistribution requires IEEE permission.
See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
You might also like
Interpretable emotion recognition using EEG signals