IDNet: Smartphone-based gait recognition with convolutional neural networks

PATTERN RECOGNITION

卷:74

Article

Gadaleta, Matteo¹  Rossi, Michele¹ 

[1] Univ Padua, Dept Informat Engn, Via Gradenigo 6-B, I-35131 Padua, Italy

关键词: Biometric gait analysis;    Target recognition;    Classification methods;    Convolutional neural networks;    Support vector machines;    Inertial sensors;    Feature extraction;    Signal processing;    Accelerometer;    Gyroscope;   

DOI  :  10.1016/j.patcog.2017.09.005

来源: Elsevier

【 摘 要 】

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 walking cycle extraction block, (ii) a novel feature extractor based on convolutional neural networks, (iii) a one-class support vector machine to classify walking cycles, and the coherent integration of these into (iv) a multi-stage authentication technique. IDNet is the first system that exploits a deep learning approach as universal feature extractors for gait recognition, and that combines 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% with fewer than five walking cycles. Design choices are discussed and motivated throughout, assessing their impact on the user authentication performance. (C) 2017 Elsevier Ltd. All rights reserved.

【 授权许可】

Free