The Impact of Temporal Proximity between Samples on Eye Movement Biometric Identification
| Autor: | Pawel Kasprowski |
|---|---|
| Přispěvatelé: | Institute of Informatics, Silesian University of Technology, Khalid Saeed, Rituparna Chaki, Agostino Cortesi, Sławomir Wierzchoń, TC 8 |
| Jazyk: | angličtina |
| Rok vydání: | 2013 |
| Předmět: |
Biometrics
business.industry Computer science [SHS.INFO]Humanities and Social Sciences/Library and information sciences 05 social sciences Eye movement 02 engineering and technology eye movement biometrics Machine learning computer.software_genre Identification (information) classification 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing 0501 psychology and cognitive sciences Computer vision [INFO]Computer Science [cs] Artificial intelligence business computer 050107 human factors behavioral biometrics |
| Zdroj: | Lecture Notes in Computer Science 12th International Conference on Information Systems and Industrial Management (CISIM) 12th International Conference on Information Systems and Industrial Management (CISIM), Sep 2013, Krakow, Poland. pp.77-87, ⟨10.1007/978-3-642-40925-7_8⟩ Computer Information Systems and Industrial Management ISBN: 9783642409240 CISIM |
| DOI: | 10.1007/978-3-642-40925-7_8⟩ |
| Popis: | Part 3: Biometrics and Biometrics Applications; International audience; Eye movements identification is an interesting alternative to other biometric identification methods. It compiles both physiological and behavioral aspects and therefore it is difficult to forge. However, the main obstacle to popularize this methodology is lack of general recommendations considering eye movement biometrics experiments. Another problem is lack of commonly available databases of eye movements. Different authors present their methodologies using their own datasets of samples recorded with different devices and scenarios. It excludes possibility to compare different approaches. It is obvious that the way the samples were recorded influences the overall results. This work tries to investigate how one of the elements – temporal proximity between subsequent measurements – influences the identification results. A dataset of 2556 eye movement recordings collected for over 5 months was used as the basis of analyses. The main purpose of the paper is to identify the impact of sampling and classification scenarios on the overall identification results and to recommend scenarios for creation of future datasets. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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