Siamese neural network based algorithm for user recognition by their eye blinking

Abstract The article proposes a new method for user recognition based on their unique eyelid blinking pattern. Our research aimed to develop a method that is resistant to shoulder surfing and brute force attacks, while also not requiring complex recording devices. Most user authentication methods utilizing eyelid blinking patterns are vulnerable to pattern replication attacks. On the other hand, methods using EEG sometimes require the use of complicated equipment to record the blinking event. In our study, we utilized the publicly available mEBAL database. The temporal eyelid movement patterns extracted from the samples in the database are analyzed by a Siamese neural network. The achieved results of 98.20% accuracy and 0.11 EER unequivocally demonstrate the superiority of the proposed method over other methods using eyelid blinking for user authentication.