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Devices capable of tracking the user’s gaze have become significantly more affordable over the past few years, thus broadening their application, including in-home and office computers and various customer service equipment. Although such devices have comparatively low operating frequencies and limited resolution, they are sufficient to supplement or replace classic input interfaces, such as the keyboard and mouse. The biometric application we researched verifies a user’s identity based on parameters acquired with a low-cost eye tracker. The use of the eye-tracking device in bank booths has many advantages, including the fact that eye trackers are contactless devices, which, especially in the light of the Covid pandemic, has increasing importance, in addition to providing a solution for confirming the liveness of the user. This paper describes an experiment in which 20 features extracted from eye movement data related mainly to saccades and fixations are used as a complementary biometric modality to authenticate clients at banking kiosks. Data were collected from 39 subjects while operating a banking system using engineered biometric kiosk prototypes. Authentication performance employing eye-movement tracking and parameterizing was compared for two classifiers: Support Vector Machine (SVM) and eXtreme Gradient Boosting (XGBoost). The results showed that the XGBoost-based classifier outperformed the SVM-based one regarding equal error rates (6.76% to 8% vs. 16.21 to 18.78%). Similar differences were obtained for true acceptance rates at different false acceptance rates (0.1 and 0.01), where the SVM-based classifier achieved a maximum of 81.08% and the XGBoost-based achieved 98.65%. Finally, prospects for the broader application of eye movement tracking as a biometric modality are discussed.
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- Wersja publikacji
- Accepted albo Published Version
- DOI:
- Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1109/access.2023.3309000
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- Kategoria:
- Publikacja w czasopiśmie
- Typ:
- artykuły w czasopismach
- Opublikowano w:
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IEEE Access
nr 11,
strony 93341 - 93353,
ISSN: 2169-3536 - Język:
- angielski
- Rok wydania:
- 2023
- Opis bibliograficzny:
- Odya P., Górski F., Czyżewski A.: User Authentication by Eye Movement Features Employing SVM and XGBoost Classifiers// IEEE Access -Vol. 11, (2023), s.93341-93353
- DOI:
- Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1109/access.2023.3309000
- Źródła finansowania:
-
- Płatność w ramach uczelnianego programu publikowania otwartego w wydawnictwie IEEE
- Weryfikacja:
- Politechnika Gdańska
wyświetlono 92 razy
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