On Optimal Operator for Combining Left and Right Sole Pressure Data in Biometrics Security

This paper describes optimal operator for combining left and right sole pressure data in a personal authentication method by dynamic change of sole pressure distribution while walking. The method employs a pair of right and left sole pressure distribution change data. These data are acquired by a mat-type load distribution sensor. The system extracts features based on shape of sole and weight shift from each sole pressure distribution. We calculate fuzzy degrees of right and left sole pressures for a registered person. Fuzzy if-then rules for each registered person are statistically determined by learning data set. Next, we combine the fuzzy degrees of right and left sole pressure data. In this process, we consider six combination operators. We examine which operator achieves best accuracy for the personal authentication. In the authentication system, we identify the walking persons as a registered person with the highest fuzzy degree. We verify the walking person as the target person when the combined fuzzy degree of the walking person is higher than a threshold. In our experiment, we employed 90 volunteers, and our method obtained higher authentication performance by mean and weighted sum operators. 1. Introduction Information technologies and network-based services, such as healthcare, commercial, and social services become indispensable parts of our lives. Reliable authentication of users is needed for secure access to these services to avoid compromising our privacy. Passwords and PINs are still the major authentication methods for network services. However, we have to remember a lot of passwords or PINs for several services. Moreover, the information might be stolen by shoulder surfing, keystroke logging and so on. Biometrics is an emerging technology to authenticate a person based on physical or behavioral features. While techniques using physical features such as fingerprint [1, 2] and iris can achieve high recognition accuracy, behavioral features such as signature [3], speech [4], and walking [5–10] are more user friendly. We focus on a biometric method based on sole pressure and dynamics in walking. Because walking is the most natural daily motion, biometrics systems that use walking do not require any training for authentication. This method can be conveniently used to authenticate people while passing through a door or passageway. It is available to an application where a person entering a room and walking toward a device or a computer can be authenticated and immediately logged in the room. Walking patterns can be captured with cameras [5,