A Novel Approach for Automatic Enhancement of Fingerprint Images via Deep Transfer Learning

For any Automated Fingerprint Identification System, the quality of its images is vital to ensure the proper accuracy of the whole system. When the quality of an image is not satisfactory, enhancement processes may be applied to help the extraction of the fingerprint features. There are several enhancement techniques, and their suitability depends on the features of the original fingerprint image. Choosing the best enhancement method is crucial because these procedures do not always improve the image quality, and may even worsen it. This work addresses this topic and presents a classifier based on Convolutional Neural Networks (CNNs) that automatically chooses the most suitable enhancement method for a specific image and applies it, but only if necessary. Our solution avoids an excessive human effort to select the best enhancement process and also requires no further training. We evaluated our proposal using FVC’s datasets, and results show the benefits of CNN-based feature extractors and that our solution was able to improve the quality of digital printing through the adaptive application of enhancement filters.