Abstract
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.
Highlights
Information technologies and network-based services, such as healthcare, commercial, and social services become indispensable parts of our lives
We have proposed a biometric personal authentication system based on fuzzy logic
A fuzzy degree of both sole pressure data was calculated by combination operators
Summary
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. We have to remember a lot of passwords or PINs for several services. 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 [5,6,7,8,9,10] are more user friendly
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