An Extensive Study of User Identification via Eye Movements across Multiple Datasets

Several studies have reported that biometric identification based on eye movement characteristics can be used for authentication. This paper provides an extensive study of user identification via eye movements across multiple datasets based on an improved version of method originally proposed by George and Routray. We analyzed our method with respect to several factors that affect the identification accuracy, such as the type of stimulus, the IVT parameters (used for segmenting the trajectories into fixation and saccades), adding new features such as higher-order derivatives of eye movements, the inclusion of blink information, template aging, age and gender.We find that three methods namely selecting optimal IVT parameters, adding higher-order derivatives features and including an additional blink classifier have a positive impact on the identification accuracy. The improvements range from a few percentage points, up to an impressive 9 % increase on one of the datasets.Comment: 11 pages, 5 figures, submitted to Signal Processing: Image Communicatio

Machine learning classification of user attributes via eye movements

The advent of modern eye tracking devices has spawned a plethora of new research on eye movements. Applications of these research results include the prediction of diseases, of biometrics, of gender, or of cognitive developments in children. One par- ticularly well studied topic is user identification. Another, less well studied one is gender prediction. In this thesis, a common framework to predict users and gen- der is proposed. Using this framework, we were able to improve the state-of-the-art accuracies for both user identification and gender prediction. Further, unlike previ- ous studies, the proposed approach was tested with different datasets consisting of varying stimuli. We identify several factors that affect the identification accuracy. Our main improvements in identification accuracy are due to three factors, select- ing optimal hyper-parameters of the segmentation algorithm, adding higher-order derivatives, and including blink information. For gender prediction, the thesis es- tablishes several new insights. For instance, that gender prediction is possible for prepubescent children aged 9–10. Previous research had suggested that significant gender differences in eye movements can only be observed in adults. Various factors are identified which affect the accuracy of gender prediction; for example, the length of the gaze trajectory, possible fatigue of the participant (gender prediction works better in the presence of fatigue), and the choice of feature ranking algorithms

Predicting Gender via Eye Movements

In this paper, we report the first stable results on gender prediction via eye movements. We use a dataset with images of faces as stimuli and with a large number of 370 participants. Stability has two meanings for us: first that we are able to estimate the standard deviation (SD) of a single prediction experiment (it is around 4.1 %); this is achieved by varying the number of participants. And second, we are able to provide a mean accuracy with a very low standard error (SEM): our accuracy is 65.2 %, and the SEM is 0.80 %; this is achieved through many runs of randomly selecting training and test sets for the prediction. Our study shows that two particular classifiers achieve the best accuracies: Random Forests and Logistic Regression. Our results reconfirm previous findings that females are more biased towards the left eyes of the stimuli