Abstract

In this study, an eye blinking re-identification system was proposed. A fast local binary pattern was used for feature extraction because its grayscale invariance and rotational invariance allow for the effective acquisition of feature information even in the presence of noise. Finally, a recurrent neural network and long short-term memory were used for model training. The results indicated that, compared with the model trained using static data, the models based on dynamic features were less affected by environmental noise in terms of accuracy. In addition, the model trained using the recurrent neural network was highly effective in identifying unenrolled users and achieved high overall accuracy.