Kyubark Shim

Erratum to “New Optical Methods for Liveness Detection on Fingers”

This paper is devoted to new optical methods, which are supposed to be used for liveness detection on fingers. First we describe the basics about fake finger use in fingerprint recognition process and the possibilities of liveness detection. Then we continue with introducing three new liveness detection methods, which we developed and tested in the scope of our research activities--the first one is based on measurement of the pulse, the second one on variations of optical characteristics caused by pressure change, and the last one is based on reaction of skin to illumination with different wavelengths. The last part deals with the influence of skin diseases on fingerprint recognition, especially on liveness detection.

New optical methods for liveness detection on fingers.

This paper is devoted to new optical methods, which are supposed to be used for liveness detection on fingers. First we describe the basics about fake finger use in fingerprint recognition process and the possibilities of liveness detection. Then we continue with introducing three new liveness detection methods, which we developed and tested in the scope of our research activities—the first one is based on measurement of the pulse, the second one on variations of optical characteristics caused by pressure change, and the last one is based on reaction of skin to illumination with different wavelengths. The last part deals with the influence of skin diseases on fingerprint recognition, especially on liveness detection.

Kalman filter vs. particle filter in improving K-NN indoor positioning

The Kalman filter has been widely used in estimating the state of a process and it is well known that no other algorithm can out-perform it if the assumptions of the Kalman filter hold. For a non-Gaussian estimation problem, both the extended Kalman filter and particle filter have been widely used. However, no one has performed comparison test of them. In the consequence, they arbitrarily choose one of them and apply it on their estimation process. Therefore, we have compared the performance of the Kalman filter against the performance of the particle filter. One of the practical fields on which these filters have been applied is indoor positioning. As the techniques of manufacturing mobile terminals have made a big progress, the demand for LBS (location based services) also has rapidly grown. One of the key techniques for LBS is positioning, or determining the location of the mobile terminal. Outdoor positioning is not a big burden to system developers because GPS (Global Positioning System) provides pretty accurate location information of a mobile terminal if the line of sight is not blocked. On the contrary, there is no practical solution for the indoor positioning problem. We can obtain exact location of a mobile terminal if we invest large amount of money, but this is economically not practical. One of the most practical candidate solutions for the indoor positioning problem is the WLAN (Wireless Local Area Network) based positioning methods because they do not require any special devices dedicated for indoor positioning. One of the most significant shortcomings of them is inaccuracy due to the noise on measured data. In order to improve the accuracy of WLAN based indoor positioning, both the Kalman filter and the particle filter processes have been applied on the measurements. This paper introduces our experimental results of comparing the Kalman filter and the particle filter processes in improving the accuracy of WLAN based indoor positioning so that indoor LBS developers can choose appropriate one for their applications.