Fingerprint card classification with statistical feature integration

This paper describes a fingerprint classification algorithm for an automated fingerprint identification system with a large-size ten-print card database. The classification algorithm determines a fingerprint´s pattern category based on a ridge structure analysis and a direction-based neural network, and computes additional feature characteristics such as core-delta distance, along with confidence indexes associated with each feature. A card preselector then integrates the set of obtained features after weighting them according to the features expected error and inherent selection capability, calculates the card similarity based on feature differences, statistically evaluates the conditional probability of each pair being a correct match, and selects the most similar subset of the database as candidates for minutiae matching. The experimental results confirm that effective classification capability of 0.2% false acceptance with 2% false rejection has been achieved