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Abstract.A method of person authentication based on matching scores with the fingerprint data of others is proposed. Fingerprint data of others is prepared in advance as a set of representative data. Input fingerprint data is verified against the representative data, and the person belonging to the fingerprint is confirmed from the set of matching scores. The set of scores can be thought of as a feature vector, and is compared with the feature vector already enrolled. In this paper, the mechanism of the proposed method, the person authentication system using this method are described, and its advantage. Moreover, the simple criterion and selection method of the representative data are discussed. The basic performance when general techniques are used for the classifier is FNMR-3.6% at FMR-0.1%.

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SUMMARYFingerprints are widely used as a method of personal authentication because they have the best balance among authentication performance, cost, device size, and ease of use. However, conventional fingerprint sensors have problems: the quality of the fingerprint pattern is sometimes not good due to the condition of the finger surface, such as moisture or wrinkles. These problems are caused by the operating principle of the sensors. Most fingerprint sensors detect a fingerprint pattern from the presence or absence of convexities and concavities of the finger surface. To solve these problems, we have devised a new kind of fingerprint sensor, which detects a fingerprint pattern by the use of optical characteristics inside the finger. This method is based on the new scientific discovery that a layer of skin inside the finger has a transmittance distribution corresponding to the pattern of the concavities and convexities of the finger surface. Therefore, this method can detect stable fingerprint images at all times regardless of the finger surface conditions (moisture, wrinkles). In this paper, we describe the principle of a new type of fingerprint sensor and demonstrate the effectiveness of the proposed sensor by examples.