BABW: Biometric‐Based Authentication Using DWT and FFNN

Summary

In this digital era, information security and authentication are crucial. Traditional, as well as modern, methods of identification are not capable of protecting the vast amounts of confidential data that exist worldwide. Conventional identification methods, such as passwords, secret codes, and personal identification numbers are compromised easily and also easily shared, observed, stolen, or forgotten. However, a possible alternative in determining the identities of users is to use biometrics. The brain wave as a biometric for authentication has several advantages that it cannot be stolen or replicated. Even differently abled persons shall comfortably use brain wave authentication systems rather than other authentication systems. Thus, brain wave authentication becomes one of the competing authentication systems and paves way for promising research. The brain wave signal that echoes the brain activity is captured by Electroencephalogram. Although the acquisition of EEG signal is feasible with advancement of technology, the development of authentication system using brain waves poses many challenges because of the nature of the brain waves activity of human beings. The proposed automatic biometric‐based user recognition uses brain waves activity for authentication. The acquisition of EEG signal is done and compressed using discrete wavelet transform (DWT). The Feed Forward Neural Network (FFNN) is used for pattern matching in order to provide accurate results. The accuracy of proposed BABW algorithm is 87.7% and is better than other algorithms in the literature.