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<p>Recent years have seen growing interest in contactless authentication, with several soft biometrics solutions being explored and producing promising results. This paper introduces a novel soft biometric approach for Verification that exploits users’ eye features and gaze behavior measured during the free observation of animated stimuli. The main novelty of our research lies in the specific stimuli employed: small squares moving according to various symmetrical patterns, which, to our knowledge, have never been considered before in similar investigations. In a user study involving 36 participants, we acquired gaze data through a remote eye tracker without performing any preliminary calibration. Both the closed- and open-set cases were considered. Experiments performed with various machine learning classifiers showed good verification performance, with a best accuracy of 88.84% and a best Equal Error Rate (EER) of 5.0% for the closed-set case, and a best accuracy of 87.59% and a best EER of 6.09% for the open-set case.</p>

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Gaze-based biometric authentication using symmetric dynamic stimuli

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月15日

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  • Piercarlo Dondi,
  • Hoàng Nam Lê,
  • Roberto Gentilini,
  • Marco Porta

摘要

Recent years have seen growing interest in contactless authentication, with several soft biometrics solutions being explored and producing promising results. This paper introduces a novel soft biometric approach for Verification that exploits users’ eye features and gaze behavior measured during the free observation of animated stimuli. The main novelty of our research lies in the specific stimuli employed: small squares moving according to various symmetrical patterns, which, to our knowledge, have never been considered before in similar investigations. In a user study involving 36 participants, we acquired gaze data through a remote eye tracker without performing any preliminary calibration. Both the closed- and open-set cases were considered. Experiments performed with various machine learning classifiers showed good verification performance, with a best accuracy of 88.84% and a best Equal Error Rate (EER) of 5.0% for the closed-set case, and a best accuracy of 87.59% and a best EER of 6.09% for the open-set case.