Note that the only resource is the biometric sample, and the hypothesis is that we can answer the question by looking at pixels only. The presentation attack detection problem consists of answering whether or not a captured biometric sample is genuine. Finally, we expose some of the problems with the state of the art, motivating our approach. Next, we present an overview of some relevant techniques in the literature. We start by looking at how genuine-access and attack images are created, discussing some general assumptions that are involved in software-based presentation-attack detection. Finally, the last section draws conclusions and presents possible future directions of investigation. The experimental results section analyzes and validates the methods in terms of performance and comparative experiments, considering multiple factor-disjoint protocols. The datasets section describes the datasets used in the experiments, highlighting the one specifically constructed for this work. The proposed method section presents our approach to tackle the PAD problem, with a detailed description of the proposed method and its techniques. The background section explores important concepts and outlines several PAD methods in the literature. The remainder of this article is organized as follows. Two techniques to train deep convolutional neural networks to model the problem in a purely data-driven fashion, with RGB pixels as input.Ī simple yet effective method for adapting a trained model by using a gallery of user data on the device thus heightening the discriminability of the model.Ī novel face presentation-attack detection dataset-RECOD-MPAD-that is representative of the target scenario herein, with more realistic illumination conditions.Īn extensive study of error cases, considering multiple factor-disjoint protocols, which leads to a better understanding of the problem. To further improve the effectiveness of these models in real-world situations, when they are deployed on mobile devices and are presented with images from the same user, we also propose one strategy to adapt the decision boundary to the characteristics of a specific user and of a sensor device. By using a lightweight but powerful architecture as the core of the proposed method, we ensure that inference can run with small memory footprint and in under one second in modern smartphones. We introduce a loss function that closely models the PAD objective, forcing genuine-access examples from the same device to be more compactly located in the learned feature space, while also reducing inter-device confusion. We adapt a pre-trained architecture for PAD using, during training, multi-resolution face patches, making the model more robust to changes in resolution, while also avoiding overfitting to specific facial features. We take a data-driven approach and present training techniques targeting the PAD problem. In this work, we focus on face PAD for modern smartphones, considering printed-photo and screen attacks. Moreover, most studies are based on similar handcrafted features, and do not target the mobile-device scenario. Over the last years, there have been increased research interest in face presentation attack detection (PAD), but the existing approaches have been shown not to generalize beyond the conditions represented in the public datasets used as benchmarks, as is evident in cross-dataset evaluations. That requires little technical expertise, as most people’s images are readily available on the Internet, and even a laptop display could be used as an attack medium. A PA can be made by simply showing the system an image of the device owner. However, it has become popular knowledge that face authentication systems are somewhat vulnerable to presentation attacks (PA) at the sensor level. Face authentication is a convenient way for unlocking such devices, requiring only that the owner looks at the built-in frontal camera, as in normal usage. As such, these devices must be secured, so that only the owner can access the data stored therein. Most people use them as their main medium of communication, storing conversational history, pictures, passwords, and other private data. Smartphones have become so popular that they are almost an extension of the user’s body and mind.
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