Fusion Biometric Recognition Using CNN and LSTM Algorithms

Abstract

Abstract: The automatic identification of people using their unique physical characteristics for security purposes is known as biometrics. Biometric authentication has a significant difficulty that calls for the development of more efficient methods in order to confirm the actual presence of a true legitimate trait as opposed to a fake self-manufactured synthetic or reconstructed sample. Recent developments in machine learning, computer vision, and pattern recognition have accelerated the development of the biometric recognition technology. The suggested approach intends to strengthen the security of biometric recognition frameworks by introducing two authentication. Each modality faces a unique set of difficulties. When practical, adults were used to evaluate the performance of technology and software created for infants. The type of biometric recognition technology will determine how accurate it is the effectiveness of the algorithm, the biometric trait used, and the calibre of the data collected. The recommended method beats earlier state-of-the-art approaches, and extra biometric data reveals extremely valuable information that may be used to quite efficiently discriminate actual features from fake ones