Search Results for author: Oleg V. Komogortsev

Found 8 papers, 0 papers with code

Benefits of temporal information for appearance-based gaze estimation

no code implementations24 May 2020 Cristina Palmero, Oleg V. Komogortsev, Sachin S. Talathi

The magnitude of contribution from temporal gaze trace is yet unclear for higher resolution/frame rate imaging systems, in which more detailed information about an eye is captured.

Gaze Estimation Temporal Sequences

OpenEDS2020: Open Eyes Dataset

no code implementations8 May 2020 Cristina Palmero, Abhishek Sharma, Karsten Behrendt, Kapil Krishnakumar, Oleg V. Komogortsev, Sachin S. Talathi

We present the second edition of OpenEDS dataset, OpenEDS2020, a novel dataset of eye-image sequences captured at a frame rate of 100 Hz under controlled illumination, using a virtual-reality head-mounted display mounted with two synchronized eye-facing cameras.

Gaze Estimation Gaze Prediction +2

Assessment of Shift-Invariant CNN Gaze Mappings for PS-OG Eye Movement Sensors

no code implementations4 Sep 2019 Henry K. Griffith, Dmytro Katrychuk, Oleg V. Komogortsev

Photosensor oculography (PS-OG) eye movement sensors offer desirable performance characteristics for integration within wireless head mounted devices (HMDs), including low power consumption and high sampling rates.

Hybrid PS-V Technique: A Novel Sensor Fusion Approach for Fast Mobile Eye-Tracking with Sensor-Shift Aware Correction

no code implementations17 Jul 2017 Ioannis Rigas, Hayes Raffle, Oleg V. Komogortsev

This paper introduces and evaluates a hybrid technique that fuses efficiently the eye-tracking principles of photosensor oculography (PSOG) and video oculography (VOG).

Sensor Fusion

Photosensor Oculography: Survey and Parametric Analysis of Designs using Model-Based Simulation

no code implementations17 Jul 2017 Ioannis Rigas, Hayes Raffle, Oleg V. Komogortsev

This paper presents a renewed overview of photosensor oculography (PSOG), an eye-tracking technique based on the principle of using simple photosensors to measure the amount of reflected (usually infrared) light when the eye rotates.

Method to Assess the Temporal Persistence of Potential Biometric Features: Application to Oculomotor, and Gait-Related Databases

no code implementations13 Sep 2016 Lee Friedman, Ioannis Rigas, Mark S. Nixon, Oleg V. Komogortsev

We suggest that the best way to assess temporal persistence is to perform a test-retest study, and assess test-retest reliability.

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