| Summary: | 碩士 === 元智大學 === 電機工程學系 === 106 === There are many ways to do authentication, but most of the systems verification are still based on passwords. Passwords are very valuable to hackers, and there is endless news that involving hackers stealing passwords and obtaining user information for illegal purposes. In order to solve this problem, people gradually turn their attention to the biometric authentication system with high security. With the great evolution of deep convolutional neural networks in recent years, deep convolutional features with high robustness and adaptability has been utilized as features in the liveness detection mechanism. However, a large amount of parameters and high computational complexity are less suitable for portable mobile device with offline operation.
In this paper, we use a lightweight local binary pattern based deep convolutional network to analyze real faces and fake faces. In order to evaluate our performance, we also utilized the CASIA-FASD database, REPLAY-ATTACK database as our benchmark database. Empirically, our proposed architecture not only shows that can improve the overall performance, but also significantly reduce amount of parameters in the relevant neural network method.
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