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Here, we present IDNet, a user authentication framework from smartphone-acquired motion signals. Its goal is to recognize a target user from their way of walking, using the accelerometer and gyroscope (inertial) signals provided by a commercial smartphone worn in the front pocket of the user’s trousers. IDNet features several innovations including: (i) a robust and smartphone-orientation-independent...
We present a data acquisition and signal processing framework for the authentication of users from their gait signatures (accelerometer and gyroscope data). An ankle-worn inertial measurement unit (IMU) is utilized to acquire the raw motion data, which is pre-processed and used to train a number of signal processing tools, including a convolutional neural network (CNN) for the extraction of features...
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