Summary of User Identification Via Free Roaming Eye Tracking Data, by Rishabh Vallabh Varsha Haria et al.
User Identification via Free Roaming Eye Tracking Data
by Rishabh Vallabh Varsha Haria, Amin El Abed, Sebastian Maneth
First submitted to arxiv on: 14 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Human-Computer Interaction (cs.HC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a new dataset for user identification using eye movements recorded with a wearable eye tracker on a university campus. The dataset consists of “free roaming” and “targeted roaming” tasks, where participants are asked to walk around the campus or find a specific room in a library. The authors use a Radial Basis Function Network (RBFN) as a classifier and achieve highest accuracies of 87.3% for free roaming and 89.4% for targeted roaming. These results compare favorably to those achieved in laboratory settings, which often require specialized equipment and may not be feasible in real-world scenarios. The authors also investigate the impact of including higher order velocity derivatives on user identification accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper looks at how well a computer program can figure out who is doing what just by tracking their eye movements while they walk around or search for something specific. To do this, 41 people walked around a university campus or searched for a room in a library. Their eye movements were recorded using a special device called a wearable eye tracker. The researchers used a special kind of computer model to try to figure out who was doing what based on the eye movement data. They found that the model could accurately identify the person 87.3% of the time when they walked around and 89.4% of the time when they searched for something specific. This is pretty good compared to other studies that did similar things in a lab setting. |
Keywords
* Artificial intelligence * Tracking
