Biometric Recognition via Eye Movements: Saccadic Vigor and Acceleration Cues

IF 1.9 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING ACM Transactions on Applied Perception Pub Date : 2016-03-03 DOI:10.1145/2842614
Ioannis Rigas, Oleg V. Komogortsev, R. Shadmehr
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引用次数: 49

Abstract

Previous research shows that human eye movements can serve as a valuable source of information about the structural elements of the oculomotor system and they also can open a window to the neural functions and cognitive mechanisms related to visual attention and perception. The research field of eye movement-driven biometrics explores the extraction of individual-specific characteristics from eye movements and their employment for recognition purposes. In this work, we present a study for the incorporation of dynamic saccadic features into a model of eye movement-driven biometrics. We show that when these features are added to our previous biometric framework and tested on a large database of 322 subjects, the biometric accuracy presents a relative improvement in the range of 31.6--33.5% for the verification scenario, and in range of 22.3--53.1% for the identification scenario. More importantly, this improvement is demonstrated for different types of visual stimulus (random dot, text, video), indicating the enhanced robustness offered by the incorporation of saccadic vigor and acceleration cues.
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眼动生物特征识别:眼动活力和加速线索
以往的研究表明,人眼运动可以作为动眼肌系统结构要素的宝贵信息来源,也可以为视觉注意和感知相关的神经功能和认知机制打开一扇窗。眼动驱动生物识别技术的研究领域是探索从眼动中提取个体特征并将其用于识别目的。在这项工作中,我们提出了一项将动态跳眼特征纳入眼动驱动生物识别模型的研究。我们表明,当这些特征被添加到我们之前的生物识别框架中,并在322个受试者的大型数据库中进行测试时,生物识别准确率在验证场景的31.6- 33.5%范围内相对提高,在识别场景的22.3- 53.1%范围内相对提高。更重要的是,这种改善在不同类型的视觉刺激(随机点、文本、视频)中都得到了证明,这表明视跳活力和加速线索的结合增强了鲁棒性。
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来源期刊
ACM Transactions on Applied Perception
ACM Transactions on Applied Perception 工程技术-计算机:软件工程
CiteScore
3.70
自引率
0.00%
发文量
22
审稿时长
12 months
期刊介绍: ACM Transactions on Applied Perception (TAP) aims to strengthen the synergy between computer science and psychology/perception by publishing top quality papers that help to unify research in these fields. The journal publishes inter-disciplinary research of significant and lasting value in any topic area that spans both Computer Science and Perceptual Psychology. All papers must incorporate both perceptual and computer science components.
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