论文标题
关于在多设备环境中收集的键入模式的软生物识别技术推断
On the Inference of Soft Biometrics from Typing Patterns Collected in a Multi-device Environment
论文作者
论文摘要
在本文中，我们研究了性别，主要/次要的（计算机科学，非计算机科学）的推断，在多设备环境中从117个人收集的打字模式中的打字样式，年龄和身高。前三个标识符的推断被视为分类任务，其余作为回归任务。对于分类任务，我们对六个经典机器学习（ML）和四个深度学习（DL）分类器进行基准测试。另一方面，对于回归任务，我们评估了三个ML和四个基于DL的回归器。总体实验包括两个文本输入（免费和固定）和四个设备（桌面，平板电脑，电话和组合）配置。最佳安排的准确性为96.15％，93.02％和87.80％的打字样式，性别和主要/小型/次要的精度分别为1.77岁，年龄和身高分别为2.65英寸。考虑到我们在这项工作中列出的各种应用程序方案，结果很有希望。
In this paper, we study the inference of gender, major/minor (computer science, non-computer science), typing style, age, and height from the typing patterns collected from 117 individuals in a multi-device environment. The inference of the first three identifiers was considered as classification tasks, while the rest as regression tasks. For classification tasks, we benchmark the performance of six classical machine learning (ML) and four deep learning (DL) classifiers. On the other hand, for regression tasks, we evaluated three ML and four DL-based regressors. The overall experiment consisted of two text-entry (free and fixed) and four device (Desktop, Tablet, Phone, and Combined) configurations. The best arrangements achieved accuracies of 96.15%, 93.02%, and 87.80% for typing style, gender, and major/minor, respectively, and mean absolute errors of 1.77 years and 2.65 inches for age and height, respectively. The results are promising considering the variety of application scenarios that we have listed in this work.
