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Automated Fingerprint Ridge Density Measurement Based on the Identification of Local Maxima
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Wen-jun ZHANG, Ya-bin ZHAO*, De-long LI
Science Technology and Engineering | 2025, 25(16) : 6841 - 6849
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Science Technology and Engineering | 2025, 25(16): 6841-6849
• Papers·Automation and Computational Technology •
Automated Fingerprint Ridge Density Measurement Based on the Identification of Local Maxima
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Wen-jun ZHANG, Ya-bin ZHAO*, De-long LI
Affiliations
  • Academy of Forensic Science, People's Public Security University of China, Beijing 100038, China
Published: 2025-06-08 doi: 10.12404/j.issn.1671-1815.2405373
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In the field of fingerprint recognition technology, ridge density, as one of the morphological features of fingerprints, has demonstrated increasing research value. Aiming at the problems of time-consuming and labor-intensive existing measurement methods, an algorithm based automated measurement method was proposed. The algorithm first preprocessed fingerprint images, including grayscale conversion, edge detection, noise reduction, and ridge enhancement, to improve image quality and clarity. Subsequently, it strengthened fingerprint features, performed array transformation, determined directional vectors, detects peaks, and finally plotted a grayscale fluctuation diagram to visually present the measurement results. Experimental results show that the automated measurement algorithm performs well in terms of efficiency and accuracy, exhibiting high consistency and significant statistical correlation with manual measurements. This further validates the scientific robustness and effectiveness of the automated measurement method, providing new perspectives and approaches for the automation and intelligence of fingerprint recognition.

ridge density  /  automated measurement  /  image enhancement  /  peak detection  /  statistical analysis
Wen-jun ZHANG, Ya-bin ZHAO, De-long LI. Automated Fingerprint Ridge Density Measurement Based on the Identification of Local Maxima[J]. Science Technology and Engineering, 2025 , 25 (16) : 6841 -6849 . DOI: 10.12404/j.issn.1671-1815.2405373
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Year 2025 volume 25 Issue 16
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Article Info
doi: 10.12404/j.issn.1671-1815.2405373
  • Receive Date：2024-07-17
  • Online Date：2025-07-09
  • Published：2025-06-08
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  • Received：2024-07-17
  • Revised：2025-03-09
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    Academy of Forensic Science, People's Public Security University of China, Beijing 100038, China
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小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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