Performance analysis on fingerprint identification by deep learning approach - Florence, Francis-Lothai; Kung Chuang, Ting; Emily Kiang Siew, Sing; Hai Inn, Ho; Annie, Joseph; Tengku Mohd Afendi, Zulcaffle; David Bong, Boon Liang (Inderscience Enterprises Ltd., 2025)
Achieving high accuracy in fingerprint identification remains challenging, despite various approaches that have been introduced over the years, including deep learning-based methods. These approaches can be computationally complex and may require a vast amount of training data. This study aims to evaluate the performance of deep learning-based approaches for fingerprint identification using two pretrained deep network models, i.e., GoogLeNet and ResNet18. The images in the datasets are first registered and cropped before being trained and validated. The validation rates demonstrated that the preprocessed images produced higher average validation rates compared to the original images. These images are then applied during the testing phase, resulting in nearly perfect identification rates for both models. In comparison, with only 20% of the training dataset, GoogLeNet and ResNet18 achieved 93.00% and 97.00% for the FingerDOS database, respectively. Both models obtained an 88.75% identification rate on the FVC2002 DB1A database, outperforming other methods.