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Now showing items 11-18 of 18
Palmprint verification using SIFT majority voting
(Springer-Verlag, 2012)
In this paper we illustrate the implementation of a robust, real-time biometric system for identity verification based on palmprint images. The palmprint images are preprocessed to align the major axes of hand shapes and ...
Numerical integration methods for simulation of mass-spring-damper systems
(Springer-Verlag, 2012)
The dynamics of a face are often implemented as a system of connected particles with various forces acting upon them. Animation of such a system requires the approximation of velocity and position of each particle through ...
An observation based muscle model for simulation of facial expressions
(Elsevier Science BV, 2018-05)
This study presents a novel facial muscle model for coding of facial expressions. We derive this model from unintrusive observation of human subjects in the progress of the surprise expression. We use a generic and ...
Improved microphone array design with statistical speaker verification
(Elsevier Ltd, 2021-04)
Conventional microphone array implementations aim to lock onto a source with given location and if required, tracking it. It is a challenge to identify the intended source when the location of the source is unknown and ...
Subset selection for tuning of hyper-parameters in artificial neural networks
(IEEE, 2017)
Hyper-parameters of a machine learning architecture define its design. Tuning of hyper-parameters is costly and for large data sets outright impractical, whether it is performed manually or algorithmically. In this study ...
Unsupervised textile defect detection using convolutional neural networks
(Elsevier Ltd, 2021-12)
In this study, we propose a novel motif-based approach for unsupervised textile anomaly detection that combines the benefits of traditional convolutional neural networks with those of an unsupervised learning paradigm. It ...
Exploiting epistemic uncertainty of the deep learning models to generate adversarial samples
(Springer, 2022-03)
Deep neural network (DNN) architectures are considered to be robust to random perturbations. Nevertheless, it was shown that they could be severely vulnerable to slight but carefully crafted perturbations of the input, ...
Closeness and uncertainty aware adversarial examples detection in adversarial machine learning
(Elsevier Ltd, 2022-07)
While deep learning models are thought to be resistant to random perturbations, it has been demonstrated that these architectures are vulnerable to deliberately crafted perturbations, albeit being quasi-imperceptible. These ...