Automatic activity recognition systems aim to capture the state of the user and its environment by exploiting heterogeneous sensors, and permit continuous monitoring of numerous physiological signals, where these sensors are attached to the subject's body. This can be immensely useful in healthcare applications, for automatic and intelligent daily activity monitoring for elderly people. In this paper, we present novel data analytic scheme for intelligent Human Activity Recognition (AR) using smartphone inertial sensors based on information theory based feature ranking algorithm and classifiers based on random forests, ensemble learning and lazy learning. Extensive experiments with a publicly available database 1 of human activity with smart phone inertial sensors show that the proposed approach can indeed lead to development of intelligent and automatic real time human activity monitoring for eHealth application scenarios for elderly, disabled and people with special needs.
In this paper, we propose two new approaches for extracting mouth features for authenticating the person identity with liveness checks. The novel correlated audio-lip features and tensor lip-motion features allow liveness checks to be included in the person identity authentication systems, and ensures that the biometric cues are acquired from a live person who is actually present at the time of capture. Incorporating liveness check functionality in identity authentication systems can guard the system against the advanced spoofing attempts such as manufactured or replayed videos.
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