A novel algorithm to recognise human identities via gait by body-worn accelerometers is presented. It uses acceleration information to measure human gait dynamics. Acceleration-based gait recognition is a nonintrusive biometric measurement, which is insensitive to changes of lighting conditions and viewpoint. The proposed algorithm first extracts signature points from gait acceleration signals, and then identifies the gait pattern using a signature point-based voting scheme. Experiments with a data set of 30 subjects show that the proposed algorithm significantly outperforms other existing methods and achieves a high recognition rate of 96.7% for the case of five accelerometers.Introduction: The emerging pervasive computing paradigm emphasises that computing be thoroughly integrated into everyday activities, and services be provided in an unobtrusive manner, which requires the awareness of user identity. Many miniaturised sensors, ranging in size from micrometres to millimetres, are being widely used in our daily life. In particular, wearable accelerometers have been employed in various applications, such as recognition of human daily activities [1] and gesture recognition. This Letter addresses recognition of human identities via gait measured by wearable accelerometers in pervasive computing environments. Compared with ID card, body-worn RFID, and other biometric techniques such as face, video-based gait, and fingerprint, accelerometer-based gait biometrics has several advantages. It is lighting-invariant, viewpoint-invariant and nonintrusive. It cannot be lost and stolen. However, only a little work has been done on accelerometer-based gait recognition in the literature, e.g. the correlation of the mean cycle [2], the Manhattan distance of the median cycle [3], and the dynamic time warping (DTW) approach [4].In this Letter, a novel accelerometer-based gait recognition approach is proposed, which first extracts a series of salient points (called signature points) by searching extrema in the scale space of gait acceleration signals, and then identifies the gait pattern by a signature point-based voting scheme. The signature point extraction enables the algorithm to focus on those important local patterns that characterise the individual identity. Recognition using a voting scheme makes it robust to local intra-class variation and applicable for the multi-accelerometer case.
Image fusion techniques of remote sensing data can integrate different spatial and spectral information. ALOS is a new data source with two optical sensors onboard, PRISM and AVNIR-2. PRISM has high spatial resolution, while AVNIR-2 is of multi-spectral resolution. Study on ALOS data fusion methods has potentials for promoting the applications of ALOS data. This paper aimed to explore a proper method for data fusion purpose. Five fusion methods, Brovey, PCA, SFIM, HPF and Gram-Schmidt, were tested. Visual and statistical comparisons were conducted to evaluate the effect of different fusion algorithms in terms of spatial resolution enhancement and spectral information maintenance. We found overall results of Gram-Schmidt in improving spatial resolution details and preserving spectral information were superior to the others. PCA was good at enhancing textural information, but caused much color distortion. SFIM best preserved the spectral abundance, but was less optimal at improving spatial information.
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