IDNet: Smartphone-based gait recognition with convolutional neural networks
IDNet: Smartphone-based gait recognition with convolutional neural networks
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DOI：
10.1016/j.patcog.2017.09.005
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发表时间：
2018-02-01
影响因子：
8
通讯作者：
Rossi, Michele
中科院分区：
文献类型：
--
作者：
Gadaleta, Matteo;Rossi, Michele
在这里，我们提出了IDNet，一个来自智能手机获取的运动信号的用户认证框架。它的目标是通过用户裤子前口袋里的商用智能手机提供的加速度计和陀螺仪（惯性）信号，从他们的行走方式中识别目标用户。IDNet具有几项创新，包括：（i）一个强大的和智能手机方向无关的步行周期提取模块，（ii）一个基于卷积神经网络的新型特征提取器，（iii）一个分类步行周期的一类支持向量机，以及这些的连贯集成（iv）多阶段认证技术。IDNet是第一个利用深度学习方法作为步态识别通用特征提取器的系统，并将后续步行周期的分类结果结合到多阶段决策框架中。实验结果表明，我们的方法对国家的最先进的技术的优越性，导致错误分类率（假阴性或阳性）小于0.15%，少于五个步行周期。设计选择的讨论和动机，在整个，评估其对用户身份验证性能的影响。(C)2017爱思唯尔有限公司版权所有
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.