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Found 7 papers, 0 papers with code
Analysis of Embeddings Learned by End-to-End Machine Learning Eye Movement-driven Biometrics Pipeline
This paper expands on the foundational concept of temporal persistence in biometric systems, specifically focusing on the domain of eye movement biometrics facilitated by machine learning.
Checking the Statistical Assumptions Underlying the Application of the Standard Deviation and RMS Error to Eye-Movement Time Series: A Comparison between Human and Artificial Eyes
For TXstate, 3 of 37 fixations met all assumptions.
Custom Video-Oculography Device and Its Application to Fourth Purkinje Image Detection during Saccades
Images can be processed offline for the detection of ocular features, including the pupil and corneal reflection (First Purkinje Image, P1) position.
Method to Detect Eye Position Noise from Video-Oculography when Detection of Pupil or Corneal Reflection Position Fails
We refer to this noise as RIONEPS (Rapid Irregularly Oscillating Noise of the Eye Position Signal).
Synthetic Database for Evaluation of General, Fundamental Biometric Principles
Finally, we use our synthetic database strategy to determine how many features are required to achieve particular levels of performance as the number of subjects in the database increases from 100 to 10, 000.
A Study on the Extraction and Analysis of a Large Set of Eye Movement Features during Reading
This work presents a study on the extraction and analysis of a set of 101 categories of eye movement features from three types of eye movement events: fixations, saccades, and post-saccadic oscillations.
Method to Assess the Temporal Persistence of Potential Biometric Features: Application to Oculomotor, and Gait-Related Databases
We suggest that the best way to assess temporal persistence is to perform a test-retest study, and assess test-retest reliability.
