8 October 2015 An improved Gabor enhancement method for low-quality fingerprint images
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Proceedings Volume 9675, AOPC 2015: Image Processing and Analysis; 96751J (2015) https://doi.org/10.1117/12.2199490
Event: Applied Optics and Photonics China (AOPC2015), 2015, Beijing, China
Abstract
The criminal’s fingerprints often refer to those fingerprints that are extracted from crime scene and have played an important role in police’ investigation and cracking the cases, but these fingerprints have features such as blur, incompleteness and low-contrast of ridges. Traditional fingerprint enhancement and identification methods have some limitations and the current automated fingerprint identification system (AFIS) hasn’t not been applied extensively in police’ investigation. Since the Gabor filter has drawbacks such as poor efficiency, low preciseness of the extracted ridge’s orientation parameters, the enhancements of low-contrast fingerprint images can’t achieve the desired effects. Therefore, an improved Gabor enhancement for low-quality fingerprint is proposed in this paper. Firstly, orientation image templates with different scales were used to distinguish the orientation images in the fingerprint area, and then orientation parameters of ridge were calculated. Secondly, mean frequencies of ridge were extracted based on local window of ridge’s orientation and mean frequency parameters of ridges were calculated. Thirdly, the size and orientation of Gabor filter were self-adjusted according to local ridge’s orientation and mean frequency. Finally, the poor-quality fingerprint images were enhanced. In the experiment, the improved Gabor filter has better performance for low-quality fingerprint images when compared with the traditional filtering methods.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Hao Geng, Jicheng Li, Jinwei Zhou, Dong Chen, "An improved Gabor enhancement method for low-quality fingerprint images", Proc. SPIE 9675, AOPC 2015: Image Processing and Analysis, 96751J (8 October 2015); doi: 10.1117/12.2199490; https://doi.org/10.1117/12.2199490
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