Recursive Class Connectivity Classification (R3C) Applied to Binary Image Segmentation for Improved Infant Fingerprint Enhancement
Image enhancement plays a crucial role in infant fingerprint matching, as child-specific characteristics such as smaller finger dimensions and thinner ridge structures often degrade image quality during acquisition. To address these limitations, enrollment typically depends on specialized high-resol...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11106426/ |
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| author | Joao Leonardo Harres Dall Agnol Luiz Fernando Puttow Southier Jefferson Tales Oliva Marcelo Teixeira Rodrigo Minetto Marcelo Filipak Dalcimar Casanova Erick Oliveira Rodrigues |
| author_facet | Joao Leonardo Harres Dall Agnol Luiz Fernando Puttow Southier Jefferson Tales Oliva Marcelo Teixeira Rodrigo Minetto Marcelo Filipak Dalcimar Casanova Erick Oliveira Rodrigues |
| author_sort | Joao Leonardo Harres Dall Agnol |
| collection | DOAJ |
| description | Image enhancement plays a crucial role in infant fingerprint matching, as child-specific characteristics such as smaller finger dimensions and thinner ridge structures often degrade image quality during acquisition. To address these limitations, enrollment typically depends on specialized high-resolution scanners, which most existing enhancement methods are not designed to support. Consequently, identification rates for children remain significantly lower than those achieved with adult fingerprints. This study introduces Recursive Class Connectivity Classification (R3C), a novel framework that iteratively refines binary segmentation outputs from existing enhancement methods by extending ridge structures. R3C does not require modifications to the underlying classifier and operates without training data, which is not currently available for infant fingerprints. Instead, the method improves segmentation by repeatedly feeding the classified image back into the classification process, while combining each intermediate segmentation with the original input image. Experiments conducted on three fingerprint datasets using four different enhancement classifiers show that R3C can increase the True Acceptance Rate (TAR) by up to 4% for children and over 40% for newborns, compared to using the enhancement methods alone. A qualitative analysis further demonstrates that R3C reconnects fragmented ridge patterns, improving the visual quality of segmentation. Because it functions independently of the enhancement method used, R3C provides a flexible and broadly applicable solution for improving binary segmentation. |
| format | Article |
| id | doaj-art-fbce376696c746f1b235b76bb1e4436e |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-fbce376696c746f1b235b76bb1e4436e2025-08-20T04:01:15ZengIEEEIEEE Access2169-35362025-01-011313608713610010.1109/ACCESS.2025.359491211106426Recursive Class Connectivity Classification (R3C) Applied to Binary Image Segmentation for Improved Infant Fingerprint EnhancementJoao Leonardo Harres Dall Agnol0https://orcid.org/0009-0004-6767-3477Luiz Fernando Puttow Southier1https://orcid.org/0000-0003-2420-4094Jefferson Tales Oliva2https://orcid.org/0000-0003-1574-1293Marcelo Teixeira3https://orcid.org/0000-0002-1008-7838Rodrigo Minetto4https://orcid.org/0000-0003-2277-4632Marcelo Filipak5https://orcid.org/0009-0009-9073-9127Dalcimar Casanova6https://orcid.org/0000-0002-1905-4602Erick Oliveira Rodrigues7Graduate Program in Electrical and Computer Engineering (PPGEEC), Federal University of Technology–Paraná (UTFPR), Pato Branco, BrazilGraduate Program in Electrical and Computer Engineering (PPGEEC), Federal University of Technology–Paraná (UTFPR), Pato Branco, BrazilGraduate Program in Electrical and Computer Engineering (PPGEEC), Federal University of Technology–Paraná (UTFPR), Pato Branco, BrazilGraduate Program in Electrical and Computer Engineering (PPGEEC), Federal University of Technology–Paraná (UTFPR), Pato Branco, BrazilGraduate Program in Electrical Engineering and Industrial Informatics (CPGEI), Federal University of Technology–Paraná (UTFPR), Curitiba, BrazilInfant.ID Ltda, Curitiba, BrazilGraduate Program in Electrical and Computer Engineering (PPGEEC), Federal University of Technology–Paraná (UTFPR), Pato Branco, BrazilGraduate Program in Production and Systems Engineering (PPGEPS), Federal University of Technology–Paraná (UTFPR), Pato Branco, BrazilImage enhancement plays a crucial role in infant fingerprint matching, as child-specific characteristics such as smaller finger dimensions and thinner ridge structures often degrade image quality during acquisition. To address these limitations, enrollment typically depends on specialized high-resolution scanners, which most existing enhancement methods are not designed to support. Consequently, identification rates for children remain significantly lower than those achieved with adult fingerprints. This study introduces Recursive Class Connectivity Classification (R3C), a novel framework that iteratively refines binary segmentation outputs from existing enhancement methods by extending ridge structures. R3C does not require modifications to the underlying classifier and operates without training data, which is not currently available for infant fingerprints. Instead, the method improves segmentation by repeatedly feeding the classified image back into the classification process, while combining each intermediate segmentation with the original input image. Experiments conducted on three fingerprint datasets using four different enhancement classifiers show that R3C can increase the True Acceptance Rate (TAR) by up to 4% for children and over 40% for newborns, compared to using the enhancement methods alone. A qualitative analysis further demonstrates that R3C reconnects fragmented ridge patterns, improving the visual quality of segmentation. Because it functions independently of the enhancement method used, R3C provides a flexible and broadly applicable solution for improving binary segmentation.https://ieeexplore.ieee.org/document/11106426/Binary classificationbiometricsenhancementfingerprintinfantnewborn |
| spellingShingle | Joao Leonardo Harres Dall Agnol Luiz Fernando Puttow Southier Jefferson Tales Oliva Marcelo Teixeira Rodrigo Minetto Marcelo Filipak Dalcimar Casanova Erick Oliveira Rodrigues Recursive Class Connectivity Classification (R3C) Applied to Binary Image Segmentation for Improved Infant Fingerprint Enhancement IEEE Access Binary classification biometrics enhancement fingerprint infant newborn |
| title | Recursive Class Connectivity Classification (R3C) Applied to Binary Image Segmentation for Improved Infant Fingerprint Enhancement |
| title_full | Recursive Class Connectivity Classification (R3C) Applied to Binary Image Segmentation for Improved Infant Fingerprint Enhancement |
| title_fullStr | Recursive Class Connectivity Classification (R3C) Applied to Binary Image Segmentation for Improved Infant Fingerprint Enhancement |
| title_full_unstemmed | Recursive Class Connectivity Classification (R3C) Applied to Binary Image Segmentation for Improved Infant Fingerprint Enhancement |
| title_short | Recursive Class Connectivity Classification (R3C) Applied to Binary Image Segmentation for Improved Infant Fingerprint Enhancement |
| title_sort | recursive class connectivity classification r3c applied to binary image segmentation for improved infant fingerprint enhancement |
| topic | Binary classification biometrics enhancement fingerprint infant newborn |
| url | https://ieeexplore.ieee.org/document/11106426/ |
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