Sorry, you need to enable JavaScript to visit this website.

Nowadays, speech spoofing is so common that it presents a great challenge to social security. Thus, it is of great significance to recognize a spoofed speech from a genuine one. Most of the current researches have focused on voice conversion (VC), synthesis and recapture which mimic a target speaker to break through ASV systems by increased false acceptance rates. However, there exists another type of spoofing, voice transformation (VT), that transforms a speech signal without a target in order ‘not to be recognized’ by increased false reject rates. VT has received much less attention.


This paper introduces a deep neural network based feature extraction scheme that aims to improve the trade-off between utility and privacy in speaker classification tasks. In the proposed scenario we develop a feature representation that helps to maximize the performance of a gender classifier while minimizing additional speaker


In recent years, morphing of facial images has arisen as an important attack vector on biometric systems. Detection of morphed images has proven challenging for automated systems and human experts alike. Likewise, in recent years, the importance of efficient (fast) biometric identification has been emphasised by the rapid rise and growth of large-scale biometric systems around the world.


Convolutional Neural Networks (CNN) have been applied to age-related research as the core framework. Although faces are composed of numerous facial attributes, most works with CNNs still consider a face as a typical object and do not pay enough attention to facial regions that carry age-specific feature for this particular task. In this paper, we propose a novel CNN architecture called Fusion Network (FusionNet) to tackle the age estimation problem.


This paper addresses the problem of automated recognition of faces and facial attributes by proposing a new general approach called Accumulative Local Sparse Representation (ALSR). In the learning stage, we build a general dictionary of patches that are extracted from face images in a dense manner on a grid. In the testing stage, patches of the query image are sparsely represented using a \em local dictionary. This dictionary contains similar atoms of the general dictionary that are spatially in the same neighborhood.


This paper deals with electroencephalography (EEG)-based biometric identification, using a motor imagery task, specifically
performing imaginary arms and legs movements. Deep learning methods such as convolutional neural network (CNN) is used for automatic discriminative feature extraction and person identification. An extensive set of experimental tests, performed on a large database comprising EEG data collected from 40 subjects over two different sessions taken at a week distance, shows the existence of repeatable discriminative characteristics in individuals’ brain signals.


In order to enhance the security of automatic speaker verification (ASV) systems, automatic spoofing attack detection, which discriminates the fake audio recordings from genuine human speech, has gain much attention recently. Among various ways of spoofing attacks, replay attacks are one of the most effective and economical methods. In this paper, we explore using recurrent neural networks for automatic replay spoofing attack detection.