Towards Improved Biometric Security: EEG-Based Person Identification Enhanced by Deep Learning and Facial Recognition

Abstract

Due to their inherent qualities of being secretive, vivid, and unpredictable, electroencephalogram (EEG) signals are considered a valuable tool for security-related identification. However, research on using EEG signals for person identification is still in its early stages. The challenges lie in decoding these signals accurately and implementing effective EEG-based identification methods. In recent years, EEG has been at the forefront of scientific research on User Authentication (UA), leading to innovative experiments that aim to identify individuals based on their unique brain activity in specific usage scenarios. The utilization of EEG signals, which are derived from brain activity, holds great potential for addressing contemporary security concerns in conventional knowledge-based user authentication, including the vulnerability to shoulder surfing. This research investigates a new method for person identification that combines electroencephalogram (EEG) signals with facial video. A hybrid model is proposed, incorporating features from both MobileNet and a Convolutional Neural Network with Long short-term memory (LSTM-CNN) architecture giving a person identification accuracy of 99.81%. The model is trained and tested on the 'DEAP' dataset to identify individuals by leveraging unique EEG patterns and facial features, thereby improving biometric identification through the integration of these insights.