Kaoru Uchida

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 fingerprints 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.

Image-Based Approach to Fingerprint Acceptability Assessment

Reliable acceptability assessment of an image acquired by a fingerprint scanner is one of the major requirements in securing fingerprint-based authentication systems. As a realistic solution to this, we propose and discuss a quality assessment algorithm, which, without any additional sensing hardware, extracts and analyzes features observed in a single input image. The image acceptability judgment is made as a result of discriminant function analysis of the features obtained from (1) a spatial changing pattern of gray level, which reflects the difference in the characteristics of the substance that constitutes the object, and (2) the frequency pattern of the image, which shows the existence and density of “micro-structures” on the surface of the object, such as sweat pores. Experiments show that this software-based approach is quite effective in correctly assessing the attributes of the object on the scanner, while it is platform independent and sufficiently fast.