並列摘要
In this paper, we present a fast and precise method for fingerprint classification. The proposed method directly extracts the directional information from the thinned image of the fingerprint. We use an octagon mask to search the center point of the region of interest and consider both the direction information and the singular points in the region of interest to classify the fingerprints. In the system, not only is the amount of computation reduced but also can the extracted information be used for identification on AFIS. The system has been tested all 4000 fingerprint images on the NIST special fingerprint database 4. The classification accuracy reaches 93.425% with no rejection for 4-class classification problem.
延伸閱讀
- 林峻慰（2010）。Fingerprint Image Recognition Under Interference of Overlapping Fingerprints and Block Noises〔碩士論文，國立臺北科技大學〕。華藝線上圖書館。https://doi.org/10.6841/NTUT.2010.00177
- Wu, B. F. (2012). A Study on Automatic Fingerprint Identification Systems [master's thesis, National Tsing Hua University]. Airiti Library. https://doi.org/10.6843/NTHU.2012.00021
- Wu, Q. E., Ding, L., Li, J., Qian, X., & Yang, W. (2015). Fuzzy Fingerprint Recognition. Research Journal of Information Technology, 6(2), 25-32. https://www.airitilibrary.com/Article/Detail?DocID=20413114-201505-201602190049-201602190049-25-32
- Chen, Y. T. (2022). A study of fingerprint preprocessing and feature extraction. 影像處理暨通訊期刊, 14(1), 5-9. https://www.airitilibrary.com/Article/Detail?DocID=P20121109003-N202302170002-00002
- Hsiang, C. H. (2013). A Study on the Optimal Sampling Period of the Fingerprint Positioning Algorithm [master's thesis, National Chiao Tung University]. Airiti Library. https://doi.org/10.6842/NCTU.2013.00092