Combining multiple matchers for a high security fingerprint verification system
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
The smart floor: a mechanism for natural user identification and tracking
CHI '00 Extended Abstracts on Human Factors in Computing Systems
Identity authentication using improved online signature verification method
Pattern Recognition Letters
People Identification Using Gait Via Floor Pressure Sensing and Analysis
EuroSSC '08 Proceedings of the 3rd European Conference on Smart Sensing and Context
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
On automated model-based extraction and analysis of gait
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
A new algorithm for distorted fingerprints matching based on normalized fuzzy similarity measure
IEEE Transactions on Image Processing
Fusion of static and dynamic body biometrics for gait recognition
IEEE Transactions on Circuits and Systems for Video Technology
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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 amat-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.