Tsutomu Temma

Feature extraction method for palmprint considering elimination of creases

A new method of extracting palmprint features is presented in this report. Since palmprint images have many creases which are organized like ridges, ordinary fingerprint feature extraction algorithms are unable to extract ridges. Consequently, the goal of this research is to construct a new feature extraction method which can extract ridges under these conditions. At first, the original palmprint image is divided into local images. The ridge candidates are extracted from each local image of the palmprint. Finally, only one candidate is selected as the ridge in the local image by estimating the continuity of certain properties. Experiments with palmprint images indicate that the proposed method can extract ridges better than conventional methods, especially in areas in which both ridges and creases exist.

An on-line character recognition aimed at a substitution for a billing machine keyboard

Abstract An on-line character recognition system was developed which recognized small sized characters, whose typical height was 4 mm, with a recognition rate of 98%. The system features programming flexibility and modifying a recognition logic. It was made possible by the scheme whereby proposition-test sequences were separated into both an assembly of tree node data and test subroutines. A demonstration system was also developed which enters personal data into a computer by recognizing hand-printed characters. The whole system showed a feasible substitution for a billing machine keyboard in data entry applications.

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.

Recognition By Two Stage Discriminant Analysis

Two stage discriminant analysis has been proposed for multi class recognition. In the second stage, multiple discriminant analysis is applied to the identification for each set of classes, which are not distinctly classified in the first stage. The proposed method is applied to character recognition for the method estimation. The recognition rate was 99.3% for 91 categories of alphanumerics and special symbols. The recognition speed was 20 milliseconds per character, when this analysis program was executed on image pipelined processors. It has been shown that this method is further applicable to character sequence recognition without the need for a character isolation process.