Pre-AttentiveGaze:具有瞬间视觉交互的基于凝视的身份验证数据集。

IF 6.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Junryeol Jeon, Yeo-Gyeong Noh, JooYeong Kim, Jin-Hyuk Hong
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引用次数: 0

摘要

本文提出了一个Pre-AttentiveGaze数据集。基于注视的身份验证的定义特征之一是需要快速响应。在这项研究中,我们构建了一个数据集,通过眼球运动来识别个体,通过诱发“前注意加工”来响应给定的注视刺激,在很短的时间内。研究人员从34名参与者身上收集了共76840个眼球运动样本。从数据集中,我们提取了前人研究中提出的凝视特征,对其进行预处理,并通过机器学习模型对数据集进行验证。本研究证明了该数据集的有效性,并说明了其在基于注视的视觉刺激认证中使用的潜力,这些视觉刺激会引发注意前处理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Pre-AttentiveGaze: gaze-based authentication dataset with momentary visual interactions.

Pre-AttentiveGaze: gaze-based authentication dataset with momentary visual interactions.

Pre-AttentiveGaze: gaze-based authentication dataset with momentary visual interactions.

Pre-AttentiveGaze: gaze-based authentication dataset with momentary visual interactions.

This manuscript presents a Pre-AttentiveGaze dataset. One of the defining characteristics of gaze-based authentication is the necessity for a rapid response. In this study, we constructed a dataset for identifying individuals through eye movements by inducing "pre-attentive processing" in response to a given gaze stimulus in a very short time. A total of 76,840 eye movement samples were collected from 34 participants across five sessions. From the dataset, we extracted the gaze features proposed in previous studies, pre-processed them, and validated the dataset by applying machine learning models. This study demonstrates the efficacy of the dataset and illustrates its potential for use in gaze-based authentication of visual stimuli that elicit pre-attentive processing.

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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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