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Elena N. Cherepovskaya

dblp:195/5443 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2017
0000-0003-1836-0453ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Security and privacy · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Network and information security
1 paper
Biometric security · 100%
Human-computer interaction and pervasive computing
1 paper
Wearable and physiological sensing · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Biometric security
biometric recognition
0.312017
An Approach to Biometric Identification by Using Low-Frequency Eye Tracker · IEEE Trans. Inf. Forensics Secur. 2017
Wearable and physiological sensing › eye tracking
eye movement analysis
0.112017
An Approach to Biometric Identification by Using Low-Frequency Eye Tracker · IEEE Trans. Inf. Forensics Secur. 2017

Methods — techniques the papers use, named apart from their topics

finite differences method · 0.6classifier · 0.6
YearPublicationVenuePosition
2017 An Approach to Biometric Identification by Using Low-Frequency Eye Tracker
abstract
Biometric identification is one of the most secure existing identification methods. Algorithms based on eye tracking techniques are being studied now by many authors as eye trackers had become more accessible during the last few years. This paper is related to a biometric identification approach and a problem of providing accurate identification results by using low-frequency eye tracker. This paper presents a review of the related works in the field as well as a general classification of different identification types. We stated a formal description of a saccade and an analyzed fragment of the gaze trajectory containing a saccade, which is based on the finite differences method, and revealed features of the eye movements that can be used for the identification purpose. Two classifiers are proposed and compared based on the experimental results obtained for them.
Andrey V. Lyamin, Elena N. Cherepovskaya
IEEE Trans. Inf. Forensics Secur.2