Adaptive Preprocessing for Fingerprint Recognition Guided by Quality Classification

Fingerprint recognition systems often struggle when the fingerprint image is of quality. Many systems deal with this by applying the enhancement method to every image regardless of how good or bad the image is. This one-size-fits-all approach may not work well when image quality changes a lot from one image to the other. This paper introduces a framework that first checks the quality of a fingerprint image and then picks a preprocessing method based on the quality tier: Poor, Medium or Good. A Random Forest classifier, trained using seven image-quality features was used to sort images into these three categories. On a test set of 75 subjects (600 images) that the classifier had never seen before it correctly classified 99.5% of the images. For each quality tier, we explored a set of 58 valid preprocessing sequences. These sequences were made up of five operations: normalization, denoising, contrast enhancement, ridge sharpening and segmentation. We picked the sequence that gave the Equal Error Rate (EER) on a separate tuning set. We then tested three conditions on the held-out evaluation set using the official FVC2004 matching protocol with 4,875 genuine and impostor pairs: no preprocessing (just raw images), one fixed preprocessing sequence applied to all images, and our adaptive quality-based pipeline. The adaptive pipeline performed best in the Poor-quality tier achieving an EER of 18.01% compared to 18.45% for images. However, in the medium and good tiers raw images did as well as or even better, than both the fixed and adaptive pipelines. We look into why this pattern might have happened and what it tells us about how to design preprocessing studies.