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ACM Interact. Mob. Wearable Ubiquitous Technol."],"published-print":{"date-parts":[[2024,11,21]]},"abstract":"<jats:p>Eye-tracking technology has gained significant attention in recent years due to its wide range of applications in human-computer interaction, virtual and augmented reality, and wearable health. Traditional RGB camera-based eye-tracking systems often struggle with poor temporal resolution and computational constraints, limiting their effectiveness in capturing rapid eye movements. To address these limitations, we propose EyeTrAES, a novel approach using neuromorphic event cameras for high-fidelity tracking of natural pupillary movement that shows significant kinematic variance. One of EyeTrAES's highlights is the use of a novel adaptive windowing\/slicing algorithm that ensures just the right amount of descriptive asynchronous event data accumulation within an event frame, across a wide range of eye movement patterns. EyeTrAES then applies lightweight image processing functions over accumulated event frames from just a single eye to perform pupil segmentation and tracking (as opposed to gaze-based techniques that require simultaneous tracking of both eyes). We show that these two techniques boost pupil tracking fidelity by 6+%, achieving IoU~=92%, while incurring at least 3x lower latency than competing pure event-based eye tracking alternatives [38]. We additionally demonstrate that the microscopic pupillary motion captured by EyeTrAES exhibits distinctive variations across individuals and can thus serve as a biometric fingerprint. For robust user authentication, we train a lightweight per-user Random Forest classifier using a novel feature vector of short-term pupillary kinematics, comprising a sliding window of pupil (location, velocity, acceleration) triples. Experimental studies with two different datasets (capturing eye movement across a range of environmental contexts) demonstrate that the EyeTrAES-based authentication technique can simultaneously achieve high authentication accuracy (~=0.82) and low processing latency (~=12ms), and significantly outperform multiple state-of-the-art competitive baselines.<\/jats:p>","DOI":"10.1145\/3699745","type":"journal-article","created":{"date-parts":[[2024,11,21]],"date-time":"2024-11-21T12:23:32Z","timestamp":1732191812000},"page":"1-32","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["EyeTrAES: Fine-grained, Low-Latency Eye Tracking via Adaptive Event Slicing"],"prefix":"10.1145","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1579-8989","authenticated-orcid":false,"given":"Argha","family":"Sen","sequence":"first","affiliation":[{"name":"Indian Institute of Technology Kharagpur, Kharagpur, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-2509-5117","authenticated-orcid":false,"given":"Nuwan Sriyantha","family":"Bandara","sequence":"additional","affiliation":[{"name":"Singapore Management University, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-6977-945X","authenticated-orcid":false,"given":"Ila","family":"Gokarn","sequence":"additional","affiliation":[{"name":"Singapore-MIT Alliance for Research and Technology (SMART), Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4279-2830","authenticated-orcid":false,"given":"Thivya","family":"Kandappu","sequence":"additional","affiliation":[{"name":"Singapore Management University, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1212-1769","authenticated-orcid":false,"given":"Archan","family":"Misra","sequence":"additional","affiliation":[{"name":"Singapore Management University, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,11,21]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1037\/0096-1523.15.3.529"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.image.2022.116804"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2021.3067784"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.rasd.2023.102228"},{"key":"e_1_2_1_5_1","volume-title":"Individual differences in human eye movements: An oculomotor signature? 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