Ensuring secure and reliable person authentication is a critical challenge in modern security systems. Traditional biometric systems relying on physiological traits like fingerprints, iris, and facial recognition often suffer from spoofing vulnerabilities. In contrast, electroencephalogram (EEG) signals, characterized by unique temporal and cognitive patterns, provide a robust authentication mechanism. This paper introduces DeepV-Net, a multimodal fully convolutional neural network that leverages both EEG signals and dynamic handwritten signature data acquired from Wacom devices. The proposed model integrates spatial and temporal features of EEG signals with distinctive movement-based signature patterns through an end-to-end multimodal fusion strategy. Experimental evaluations on benchmark datasets demonstrate that DeepV-Net outperforms unimodal approaches and state-of-the-art authentication methods, achieving a training accuracy of 99.1% and a validation accuracy of 93.3%. These findings highlight the complementary nature of EEG and signature modalities, paving the way for more secure and efficient biometric authentication systems.

Person Authentication, EEG, Hand-Written Signature, Multimodal, DeepV-Net