Gait-Based Authentication for Smart Locks Using Accelerometers in Two Devices
Gait-Based Authentication for Smart Locks Using Accelerometers in Two Devices
复制标题
在两个设备中使用加速计对智能锁进行基于步态的身份验证
DOI：
10.1007/978-3-030-29029-0_26
复制
发表时间：
2019
期刊：
影响因子：
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通讯作者：
Park Mirang
中科院分区：
文献类型：
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作者：
Watanabe Kazuki;Nagatomo Makoto;Aburada Kentaro;Okazaki Naonobu;Park Mirang
智能锁可以电子打开和关闭。指纹或面部认证对于智能锁来说是不方便的，因为它需要用户在门前停几秒钟并移除某些附件（例如，手套、太阳镜）。提出了一种基于步态特征的用户身份认证方法。传统的基于步态的认证方法具有较低的识别精度。所提出的基于步态的认证方法使用智能电话和可穿戴设备（即，智能手表）。我们从采集的加速度数据中提取了31个特征，并计算了各种机器学习算法的识别精度。使用随机森林获得的最高准确率为95.3%。我们发现，最大间隔，最小间隔和最小值的识别精度的贡献最大，方差，中位数和标准差的贡献最小。
Smart locks can be opened and closed electronically. Fingerprint or face authentication is inconvenient for smart locks because it requires the user to stop for several seconds in front of the door and remove certain accessories (e.g., gloves, sunglasses). This study proposes a user authentication method based on gait features. Conventional gait-based authentication methods have low identification accuracy. The proposed gait-based authentication method uses accelerometers in a smartphone and a wearable device (i.e., smartwatch). We extracted 31 features from the acquired acceleration data and calculated identification accuracy for various machine-learning algorithms. The highest accuracy was 95.3%, obtained using random forest. We found that the maximum interval, minimum interval, and minimum value had the highest contributions to identification accuracy, and variance, median, and standard deviation had the lowest contributions.