This thesis focuses on biometric verification of subjects based on saccadic eye movements. Verification corresponds to two-class classification to recognize an authenticated user and to classify other subjects as impostors. Compared with other biometric signals or data, the possible advantages of eye movements can be as follows: harder to imitate, easier processing and faster computation. The thesis describes a procedure to use variables of saccade eye movements recorded. It analyses the variabilities between electro-oculography (EOG) and video-oculography (VOG) signals: i.e. eye movements were recorded with skin electrodes or with two special video cameras. When a signal was recorded with a low-frequency video camera device simulating a web camera, the sampling frequency of signals was enhanced using interpolation.
The techniques of signal processing and statistics were also applied to analysis. In order to evaluate biometric accuracy, the test procedures for true positive rate (TPR) and true negative rate (TNR) were designed separately. Many classification methods were explored for verification performance, including both modified simple methods such as k-nearest neighbour searching and advanced methods such as neural networks and support vector machines. Approaches and other details in the verification procedure were improved through multiple tests and comparisons of the verification accuracies. Optimal parameters and settings of the classification methods used were found. With more and more saccades and subjects collected into training sets, a high TNR accuracy was gained, which was close to 95% at its best. It showed that, using saccade eye movements, it was possible to distinguish between an authenticated user and impostors.
On the other hand, after multiple recordings of subjects, the high accuracy of TPR – close to 90% – also confirmed that an authenticated user can be recognized notwithstanding the variability of variable values of saccades between different sessions. Finally, better results given by signals with a relatively high sampling frequency of 250 Hz were obtained, and this could allow user verification based on eye movements to be applied in practice, along with the development of eye movement video cameras in future.