Abstract:
The practice of automatically recognizing the correct person using computational methods based on features maintained in
computer systems is known as person authentication. Security, robustness, privacy, and non-forgery are the critical aspects of any person
authentication system. Traditional biometric-based systems are dependent on the use of a single modality, which may be lacking in
the ability to provide high security. These systems are vulnerable to noise and can be readily exploited. An optimization-enabled deep
learning-based multimodal person authentication system is presented to solve these disadvantages. Here, a combination of brainwave
signals and fingerprint images are utilized for providing improved security. A Deep Maxout Network (DMN) is utilized for performing
person authentication on both modalities and the output obtained is fused using cosine similarity to attain the final result. The African
vultures-Aquila Optimization (AVAO) algorithm is a unique optimization algorithm for updating the DMN weights. To construct the
algorithm, the African Vulture Optimization Algorithm (AVOA) techniques are updated according to the extended exploration capabilities
of the Aquila Optimizer (AO). The presented multimodal person authentication system achieves an accuracy of 0.926, sensitivity of
0.940, specificity of 0.928, and F1-score of 0.921, demonstrating exceptional performance. The experimental study also indicates the
performance evaluation comparison of AVAO with the prevailing techniques such as Multi-task EEG-based Authentication, Multi model based fusion, Multi-biometric system, and Visual secret sharing and super-resolution model based on various metrics.
