This paper discusses an application of fingerprint identification technology to enhanced human-machine interaction, and also to information systems, specifically to a mobile authentication terminal for secure networked services and to digital appliances. A "Fingerprint User Interface (FpUI)," exploits information regarding not only who put a finger on its sensor but which specific finger it was. With this user-friendly interface, a user can assign commands, data objects, status, or personalized settings to individual fingers. A functional architecture for a mobile authentication terminal, "Pocket-PID," with fingerprint identification capability is proposed which features an easy-to-use FpUI and high security, where the identification function is totally enclosed within the unit. This enables a user's identity authenticated without any possibility of actual fingerprint data being disclosed. The Pocket-PID facilitates implementation of networked services based on secure biometric user identification.

The applications of biometrics in the real world include various types of large-scale "one-to-many" identification, which require high performance classification technology. This paper presents a system with a classification algorithm that integrates multiple features observed in a set of fingerprints and uses them, to pre-select candidates, for more efficient personal identification from a very large fingerprint enrollment database. The algorithm determines a fingerprint's pattern type by using both ridge structure analysis and direction-based neural networks. It measures such additional feature characteristics as core-delta distance and ridge counts in parallel, along with confidence indexes associated with each feature. The pre-selector then integrates the set of obtained features from multiple fingers, after weighting them according to each feature's inherent ability to contribute to the selection process and the expected errors in observations of that feature. The system calculates the similarity between pairs of sets on the basis of feature differences, statistically evaluates the conditional probability of each pair being a correct match, and selects most similar collection of candidates for detailed matching. Experimental results confirm that it achieves an effective pre-selecting capability of 0.2% average selection (false acceptance or penetration) rate with 2% selection error (false rejection) rate.