Applied research
Neonatal biometrics and fingerprint recognition
Infant.ID and UTFPR-Pato Branco
Infant.ID maintains scientific collaboration with researchers from the Federal Technological University of Paraná (UTFPR), Pato Branco campus, focused on the development of technologies for the acquisition, processing, improvement, and recognition of fingerprints of newborns and children.
The collaboration between Infant.ID and UTFPR-Pato Branco brings academic research and real-world operations closer together, allowing algorithms to be developed and evaluated based on neonatal biometric data obtained in practical conditions of use.
By September 2026, this collaboration resulted in publications in international journals and conferences of relevance, as well as the construction of a longitudinal database of high-resolution neonatal fingerprints.
Main publications
Recursive Class Connectivity Classification (R3C) Applied to Binary Image Segmentation for Improved Infant Fingerprint Enhancement
IEEE Access, 2025
It proposes a method for segmenting and enhancing the ridges and valleys of children's fingerprints, with a direct impact on the quality of the images and the performance of biometric recognition.
DOI: 10.1109/ACCESS.2025.3594912
A Systematic Literature Review on Neonatal Fingerprint Recognition
ACM Computing Surveys, 2025
A comprehensive systematic review of the state of the art in neonatal fingerprint recognition, covering sensors, resolution, processing, datasets, matching, and the main challenges in the field.
DOI: 10.1145/3735551
A Super-Resolution Approach for Image Resizing of Infant Fingerprints with Vision Transformers
IEEE Access, 2025
Investigates super-resolution techniques based on Vision Transformers for the preservation and recovery of biometric features in images of children's fingerprints.
DOI: 10.1109/ACCESS.2025.3561206
Analyzing Similarity and Quality of Neonate Fingerprint Images Subject to a Super-Resolution Technique
IEEE BIOSIG, 2024
It evaluates the impact of super-resolution techniques on the quality and similarity of biometric features in digital fingerprint images of newborns.
DOI: 10.1109/BIOSIG61931.2024.10786743
Iterative Framework for Data Augmentation of Segmented Fingerprints
Information Systems Workshop — WSIS/SBC, 2024
It presents an iterative data-augmentation strategy for segmented images of fingerprints, supporting the training of artificial intelligence models.
DOI: 10.5753/wsis.2024.33666
A Mathematical Toolkit to Improve the Similarity Analysis for Fingerprint Images
Information Systems Workshop — WSIS/SBC, 2024
It explores mathematical methods for the analysis of similarity between images of fingerprints and the quantitative evaluation of processing techniques.
DOI: 10.5753/wsis.2024.33664