The purpose of this paper is to establish an access control system by using only fingerprint identification. In order to minimize the identification time, we propose a new fingerprint classification suitable for a personal computer, and the real machine by using the classification is introduced. Our classification is implemented by only cores which are one of the features on fingerprint pattern. Therefore, it classifies all fingerprints into one of 11 classes rapidly on a personal computer. In the machine, an input fingerprint is classified and compared with ones registered in the same class. If both the input fingerprint and the registered one match, the person is allowed entry to the restricted area. Simulation results show that 443 fingerprint patterns (45 persons) are classified completely and rapidly. And the machine is effective and useful as identifier for home and room security.

Frequently interrupting someone who is busy will decrease his or her productivity. To minimize this risk, a number of interruptibility estimation methods based on PC activity such as typing or mouse clicks have been developed. However, these estimation methods do not take account of the effect of conversations in relation to the interruptibility of office workers engaged in intellectual activities such as scientific research. This study proposes an interruptibility estimation method that takes account of the conversation status. Two conversation indices, “In conversation” and “End of conversation” were used in a method that we developed based on our analysis of 50 hours worth of recorded activity. Experiments, using the conversation status as judged by the Wizard-of-OZ method, demonstrated that the estimation accuracy can be improved by the two indices. Furthermore, an automatic conversation status recognition system was developed to replace the Wizard-of-OZ procedure. The results of using it for interruptibility estimation suggest the effectiveness of the automatically recognized conversation status.