  Gaze recognition can significantly reduce the amount of eye movement data for
a better understanding of cognitive and visual processing. Gaze recognition is
an essential precondition for eye-based interaction applications in virtual
reality. However, the three-dimensional characteristics of virtual reality
environments also pose new challenges to existing recognition algorithms. Based
on seven evaluation metrics and the Overall score (the mean of the seven
normalized metric values), we obtain optimal parameters of three existing
recognition algorithms (Velocity-Threshold Identification, Dispersion-Threshold
Identification, and Velocity & Dispersion-Threshold Identification) and our
modified Velocity & Dispersion-Threshold Identification algorithm. We compare
the performance of these four algorithms with optimal parameters. The results
show that our modified Velocity & Dispersion-Threshold Identification performs
the best. The impact of interface complexity on classification results is also
preliminarily explored. The results show that the algorithms are not sensitive
to interface complexity.
