Secure Multimodal Biometric Authentication System Involving Ear, Fingerprint and Voice Recognition in Cloud Computing
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Secure multimodal biometric authentication is a process of using multiple biometric traits to authenticate a user's identity. This approach offers increased security by combining the strengths of different biometric authentication techniques, such as fingerprint recognition, iris recognition, face recognition, voice recognition, and behavioral biometrics. By combining multiple biometric traits, the risk of false positives and false negatives can be reduced, providing a more reliable and secure authentication process.
Machine learning and artificial intelligence algorithms can be used to develop secure multimodal biometric authentication systems that can adapt to changing user behavior and environmental conditions. Deep learning techniques can also be used to enhance the accuracy and efficiency of biometric recognition.
Cryptography plays a vital role in securing the biometric data and ensuring the privacy of the users. The biometric data should be encrypted before transmission, and the encryption keys must be securely stored and managed.
Overall, secure multimodal biometric authentication can provide a reliable and secure authentication process for user identification and access control. The combination of different biometric traits and machine learning algorithms can enhance the accuracy and efficiency of the authentication process, ensuring the privacy and security of the users
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