Deep Representations for Iris, Face, and Fingerprint Spoofing Detection
David Menotti,Giovani Chiachia,Allan Pinto,William Robson Schwartz,Helio Pedrini,Alexandre X. Falcão,Anderson Rocha +6 more
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In this paper, the authors proposed two deep learning approaches for spoofing detection of iris, face, and fingerprint modalities based on a very limited knowledge about biometric spoofing at the sensor.Abstract:
Biometrics systems have significantly improved person identification and authentication, playing an important role in personal, national, and global security. However, these systems might be deceived (or "spoofed") and, despite the recent advances in spoofing detection, current solutions often rely on domain knowledge, specific biometric reading systems, and attack types. We assume a very limited knowledge about biometric spoofing at the sensor to derive outstanding spoofing detection systems for iris, face, and fingerprint modalities based on two deep learning approaches. The first approach consists of learning suitable convolutional network architectures for each domain, while the second approach focuses on learning the weights of the network via back-propagation. We consider nine biometric spoofing benchmarks --- each one containing real and fake samples of a given biometric modality and attack type --- and learn deep representations for each benchmark by combining and contrasting the two learning approaches. This strategy not only provides better comprehension of how these approaches interplay, but also creates systems that exceed the best known results in eight out of the nine benchmarks. The results strongly indicate that spoofing detection systems based on convolutional networks can be robust to attacks already known and possibly adapted, with little effort, to image-based attacks that are yet to come.read more
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Journal ArticleDOI
Secure Face Unlock: Spoof Detection on Smartphones
TL;DR: An efficient face spoof detection system on an Android smartphone based on the analysis of image distortion in spoof face images and an unconstrained smartphone spoof attack database containing more than 1000 subjects are built.
Proceedings ArticleDOI
An original face anti-spoofing approach using partial convolutional neural network
TL;DR: This work extracts the deep partial features from the convolutional neural network (CNN) to distinguish the real and fake faces and uses the block principle component analysis (PCA) method to reduce the dimensionality of features that can avoid the over-fitting problem.
Journal ArticleDOI
Integration of image quality and motion cues for face anti-spoofing
TL;DR: An extendable multi-cues integration framework for face anti-spoofing using a hierarchical neural network is proposed, which can fuse image quality cues and motion cues for liveness detection.
Journal ArticleDOI
Convolutional Neural Network for Finger-Vein-Based Biometric Identification
TL;DR: A convolutional-neural-network-based finger-vein identification system is proposed and the accuracy achievable with the proposed approach can go beyond 95% correct identification rate for all the four considered publicly available databases.
Posted Content
Capsule-Forensics: Using Capsule Networks to Detect Forged Images and Videos
TL;DR: The method introduced in this paper uses a capsule network to detect various kinds of spoofs, from replay attacks using printed images or recorded videos to computer-generated videos using deep convolutional neural networks.
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