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Found 16 papers, 3 papers with code
Device (In)Dependence of Deep Learning-based Image Age Approximation
In a previous work, we have shown that the presence of strong in-field sensor defects is irrelevant for a CNN to predict the age class.
Exploring Deep Learning Image Super-Resolution for Iris Recognition
In this work we test the ability of deep learning methods to provide an end-to-end mapping between low and high resolution images applying it to the iris recognition problem.
Content Bias in Deep Learning Image Age Approximation: A new Approach Towards better Explainability
In the context of temporal image forensics, it is not evident that a neural network, trained on images from different time-slots (classes), exploits solely image age related features.
Deep Learning in the Field of Biometric Template Protection: An Overview
Biometric systems utilising deep learning have been shown to achieve auspicious recognition accuracy, surpassing human performance.
Advanced Image Quality Assessment for Hand- and Fingervein Biometrics
Natural Scene Statistics commonly used in non-reference image quality measures and a deep learning based quality assessment approach are proposed as biometric quality indicators for vasculature images.
Experimental analysis regarding the influence of iris segmentation on the recognition rate
In this study the authors will look at the detection and segmentation of the iris and its influence on the overall performance of the iris-biometric tool chain.
Iris super-resolution using CNNs: is photo-realism important to iris recognition?
The use of low-resolution images adopting more relaxed acquisition conditions such as mobile phones and surveillance videos is becoming increasingly common in iris recognition nowadays.
Super-Resolution and Image Re-projection for Iris Recognition
Several recent works have addressed the ability of deep learning to disclose rich, hierarchical and discriminative models for the most diverse purposes.
Extensive Threat Analysis of Vein Attack Databases and Attack Detection by Fusion of Comparison Scores
The last decade has brought forward many great contributions regarding presentation attack detection for the domain of finger and hand vein biometrics.
Using CNNs to Identify the Origin of Finger Vein Image
We study the finger vein (FV) sensor model identification task using a deep learning approach.
Identifying the Origin of Finger Vein Samples Using Texture Descriptors
Identifying the origin of a sample image in biometric systems can be beneficial for data authentication in case of attacks against the system and for initiating sensor-specific processing pipelines in sensor-heterogeneous environments.
Two-stage CNN-based wood log recognition
The proof of origin of logs is becoming increasingly important.
Enabling Fingerprint Presentation Attacks: Fake Fingerprint Fabrication Techniques and Recognition Performance
In this work we evaluate five different commercial-off-the-shelf fingerprint scanners based on different sensing technologies, including optical, optical multispectral, passive capacitive, active capacitive and thermal regarding their susceptibility to presentation attacks using fake fingerprint representations.
Improving Endoscopic Decision Support Systems by Translating Between Imaging Modalities
We investigate if models can be trained on virtual (or a mixture of virtual and real) samples to improve overall accuracy in a setting with limited labeled training data.
Deep Learning with Topological Signatures
Inferring topological and geometrical information from data can offer an alternative perspective on machine learning problems.
Proceedings of The 39th Annual Workshop of the Austrian Association for Pattern Recognition (OAGM), 2015
The 39th annual workshop of the Austrian Association for Pattern Recognition (OAGM/AAPR) provides a platform for presentation and discussion of research progress as well as research projects within the OAGM/AAPR community.
