ACM应用感知TAP(ACM Transactions on Applied Perception)旨在通过发表有助于统一这些领域研究的高质量论文来增强计算机科学与心理学/感知之间的协同作用。该期刊发表跨学科研究,在跨计算机科学和感知心理学的任何主题领域都具有重大而持久的价值。所有论文都必须包含感知和计算机科学两个部分。主题包括但不限于:视觉感知:计算机图形学,科学/数据/信息可视化,数字成像,计算机视觉,立体和3D显示技术。听觉感知:听觉显示和界面,听觉听觉编码,空间声音,语音合成和识别。触觉:触觉渲染,触觉输入和感知。感觉运动知觉:手势输入,身体运动输入。感官感知:感官整合,多模式渲染和交互。 官网地址:http://dblp.uni-trier.de/db/journals/tap/

In this paper, we propose a deep learning approach for smartphone user identification based on analyzing motion signals recorded by the accelerometer and the gyroscope, during a single tap gesture performed by the user on the screen. We transform the discrete 3-axis signals from the motion sensors into a gray-scale image representation which is provided as input to a convolutional neural network (CNN) that is pre-trained for multi-class user classification. In the pre-training stage, we benefit from different users and multiple samples per user. After pre-training, we use our CNN as feature extractor, generating an embedding associated to each single tap on the screen. The resulting embeddings are used to train a Support Vector Machines (SVM) model in a few-shot user identification setting, i.e. requiring only 20 taps on the screen during the registration phase. We compare our identification system based on CNN features with two baseline systems, one that employs handcrafted features and another that employs recurrent neural network (RNN) features. All systems are based on the same classifier, namely SVM. To pre-train the CNN and the RNN models for multi-class user classification, we use a different set of users than the set used for few-shot user identification, ensuring a realistic scenario. The empirical results demonstrate that our CNN model yields a top accuracy of 89.75% in multi-class user classification and a top accuracy of 96.72% in few-shot user identification. In conclusion, we believe that our system is ready for practical use, having a better generalization capacity than both baselines.

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