Face Authentication with Salient Local Features and Static Bayesian Network
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Face recognition is a mature field in biometrics in which several systems have been proposed over the last three decades.
Such systems are extremely reliable under controlled recording conditions and it has been deployed in the field in critical tasks, suc ...
The extremely high recognition accuracy achieved by modern, convolutional neural network (CNN) based face recognition (FR) systems has contributed significantly to the adoption of such systems in a variety of applications, from mundane activities like unlo ...
With face-recognition (FR) increasingly replacing fingerprint sensors for user-authentication on mobile devices, presentation attacks (PA) have emerged as the single most significant hurdle for manufacturers of FR systems. Current machine-learning based pr ...
User authentication is an important step to protect information and in this regard face biometrics is advantageous. Face biometrics is natural, easy to use and less human-invasive. Unfortunately, recent work revealed that face biometrics is quite vulnerabl ...
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We propose a method for learning non-linear face geometry representations using deep generative models. Our model is a variational autoencoder with multiple levels of hidden variables where lower layers capture global geometry and higher ones encode more l ...
Due to its convenience, biometric authentication, especial face authentication, has become increasingly mainstream and thus is now a prime target for attackers. Presentation attacks and face morphing are typical types of attack. Previous research has shown ...
Morphing attacks is a threat to biometric systems where the biometric reference in an identity document can be altered. This form of attack presents an important issue in applications relying on identity documents such as border security or access control. ...
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We propose a method for learning non-linear face geometry representations using deep generative models. Our model is a variational autoencoder with multiple levels of hidden variables where lower layers capture global geometry and higher ones encode more l ...
