Paper
Biometric Recognition Through Eye Movements Using a Recurrent Neural Network
Published Nov 1, 2018 · Shaohua Jia, D. Koh, Amanda Seccia
2018 IEEE International Conference on Big Knowledge (ICBK)
28
Citations
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Abstract
Eye movement biometrics have traditionally been tackled by using handcrafted features which lead to complex computation and heavy reliance on experimental design. The authors of this study present a general recurrent neural network framework for biometric recognition through eye movements whereby the dynamic features and temporal dependencies are automatically learned from a short data window extracted from a sequence of raw eye movement signals. The model works in a task-independent manner by using short-term feature vectors combined with using different stimuli in training and testing. The model is trained end-to-end using backpropagation and mini-batch gradient descent. We evaluate our model on a dataset with 32 subjects presented with static images, and the results show that our deep learning model significantly outperforms previous methods. The achieved Rank-1 Identification Rate (Rank-1 IR) for the identification scenario is 96.3% and the Equal Error Rate (EER) for the verification scenario is 0.85%.
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