A Metric Learning Approach to Eye Movement Biometrics
| Autor: | Samantha Aziz, Henry Griffith, Oleg V. Komogortsev, Dillon J. Lohr |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Biometrics
business.industry Computer science Feature vector 05 social sciences Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Word error rate Eye movement Pattern recognition 02 engineering and technology Facial recognition system 050105 experimental psychology ComputingMethodologies_PATTERNRECOGNITION Multilayer perceptron Metric (mathematics) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing 0501 psychology and cognitive sciences Artificial intelligence business |
| Zdroj: | IJCB |
| DOI: | 10.1109/ijcb48548.2020.9304859 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|