Here, we present IDNet, an original user authentication framework from smartphone-acquired motion signals. Its goal is to recognize a target user from her/his 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. Our design features several innovations including: a robust and smartphone-orientation-independent walking cycle extraction block, a novel feature extractor based on convolutional neural networks, a one-class support vector machine to classify walking cycles, and the coherent integration of these into a multi-stage authentication system. Our system exploits convolutional neural networks as universal feature extractors for gait recognition, and uses classification results from subsequent walking cycles into a multi-stage decision making framework. Experimental results show the superiority of our approach against state-of-the-art techniques, leading to misclassification rates (either false negatives or positives) smaller than 0.15% in fewer than five walking cycles. Design choices are discussed and motivated throughout, assessing their impact on the authentication performance.
Gadaleta and Rossi adopted the DCNN to recognize a target user based on the way of their walking utilizing the accelerometer and gyroscope data of smartphone REF .
IDNet: Smartphone-based Gait Recognition with Convolutional Neural Networks
ArXiv
ArXiv
Mathematics Computer Science