skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: A Metric Learning Approach to Eye Movement Biometrics
Metric learning is a valuable technique for enabling the ongoing enrollment of new users within biometric systems. While this approach has been heavily employed for other biometric modalities such as facial recognition, applications to eye movements have only recently been explored. This manuscript further investigates the application of metric learning to eye movement biometrics. A set of three multilayer perceptron networks are trained for embedding feature vectors describing three classes of eye movements: fixations, saccades, and post-saccadic oscillations. The network is validated on a dataset containing eye movement traces of 269 subjects recorded during a reading task. The proposed algorithm is benchmarked against a previously introduced statistical biometric approach. While mean equal error rate (EER) was increased versus the benchmark method, the proposed technique demonstrated lower dispersion in EER across the four test folds considered herein.  more » « less
Award ID(s):
1714623
PAR ID:
10285748
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
IEEE International Joint Conference on Biometrics (IJCB)
Page Range / eLocation ID:
1 to 7
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; (, ACM Symposium on Eye Tracking Research and Applications)
    null (Ed.)
    Texture-based features computed on eye movement scan paths have recently been proposed for eye movement biometric applications. Feature vectors were extracted within this prior work by computing the mean and standard deviation of the resulting images obtained through application of a Gabor filter bank. This paper describes preliminary work exploring an alternative technique for extracting features from Gabor filtered scan path images. Namely, features vectors are obtained by downsampling the filtered images, thereby retaining structured spatial information within the feature vector. The proposed technique is validated at various downsampling scales for data collected from 94 subjects during free-viewing of a fantasy movie trailer. The approach is demonstrated to reduce EER versus the previously proposed statistical summary technique by 11.7% for the best evaluated downsampling parameter. 
    more » « less
  2. ; ; ; (, Society for Neuroscience Annual Meeting 2018)
    Instant access to personal data is a double-edged sword and it has transformed society. It enhances convenience and interpersonal interactions through social media, while also making us all more vulnerable to identity theft and cybercrime. The need for hack-resistant biometric authentication is greater than ever. Previous studies have demonstrated that eye movements differ between individuals, so the characterization eye movements might provide a highly secure and convenient approach to personal identification, because eye movements are generated by the owner’s living brain in real-time and are therefore extremely difficult to imitate by hackers. To study the potential of eye movements as a biometric tool, we characterized the eye movements of 18 participants. We examined an entire battery of oculomotor behaviors, including the unconscious eye movements that occur during ocular fixation; this resulted in a high precision oculomotor signature that can identify individuals. We show that one-versus-one machine learning classification, applied with a nearest neighbor statistic, yielded an accuracy of >99% based with ~25minute sessions, during which participants executed fixations, visual pursuits, free viewing of images, etc. Even if we just examine the ~3 minutes in which participants executed the fixation task by itself, discrimination accuracy was higher than 96%. When we further split the fixation data randomly into 30 sec chunks, we obtained a remarkably high accuracy of 92%. Because eye-trackers provide improved spatial and temporal resolution with each new generation, we expect that both accuracy and the minimum sample duration necessary for reliable oculomotor biometric verification can be further optimized. 
    more » « less
  3. ; ; (, ACM Symposium on Eye Tracking Research and Applications)
    null (Ed.)
    This paper introduces a novel eye movement dataset collected in virtual reality (VR) that contains both 2D and 3D eye movement data from over 400 subjects. We establish that this dataset is suitable for biometric studies by evaluating it with both statistical and machine learning–based approaches. For comparison, we also include results from an existing, similarly constructed dataset. 
    more » « less
  4. ; ; (, 2022 Symposium on Eye Tracking Research and Applications)
    Iris-based biometric authentication is a wide-spread biometric modality due to its accuracy, among other benefits. Improving the resistance of iris biometrics to spoofing attacks is an important research topic. Eye tracking and iris recognition devices have similar hardware that consists of a source of infra-red light and an image sensor. This similarity potentially enables eye tracking algorithms to run on iris-driven biometrics systems. The present work advances the state-of-the-art of detecting iris print attacks, wherein an imposter presents a printout of an authentic user’s iris to a biometrics system. The detection of iris print attacks is accomplished via analysis of the captured eye movement signal with a deep learning model. Results indicate better performance of the selected approach than the previous state-of-the-art. 
    more » « less
  5. ; (, Proceedings of the Workshop on Eye Movements in Programming (EMIP'18))
    Recently, eye-tracking analysis for finding the cognitive load and stress while problem-solving on the whiteboard during a technical interview is finding its way in software engineering society. However, there is no empirical study on analyzing how much the interview setting characteristics affect the eye-movement measurements. Without knowing that, the results of a research on eye-movement measurements analysis for stress detection will not be reliable. In this paper, we analyzed the eye-movements of 11 participants in two interview settings, one on the whiteboard and the other on the paper, to find out if the characteristics of the interview settings affect the analysis of participants' stress. To this end, we applied 7 Machine Learning classification algorithms on three different labeling strategies of the data to suggest researchers of the domain a useful practice of checking the reliability of the eye-measurements before reporting any results. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email