Zinelabidine Boulkenafet

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Research on face spoofing detection has mainly been focused on analyzing the luminance of the face images, hence discarding the chrominance information which can be useful for discriminating fake faces from genuine ones. In this work, we propose a new face anti-spoofing method based on color texture analysis. We analyze the joint color-texture information(More)
Research on non-intrusive software-based face spoofing detection schemes has been mainly focused on the analysis of the luminance information of the face images, hence discarding the chroma component, which can be very useful for discriminating fake faces from genuine ones. This paper introduces a novel and appealing approach for detecting face spoofing(More)
The vulnerabilities of face biometric authentication systems to spoofing attacks have received a significant attention during the recent years. Some of the proposed countermeasures have achieved impressive results when evaluated on intratests, i.e., the system is trained and tested on the same database. Unfortunately, most of these techniques fail to(More)
Recently deep Convolutional Neural Networks have been successfully applied in many computer vision tasks and achieved promising results. So some works have introduced the deep learning into face anti-spoofing. However, most approaches just use the final fully-connected layer to distinguish the real and fake faces. Inspired by the idea of each convolutional(More)
Audiovisual speech synchrony detection is an important liveness check for talking face verification systems in order to make sure that the input biometric samples are actually acquired from the same source. In prior work, the used visual speech features have been mainly describing facial appearance or mouth shape in frame-wise manner, thus ignoring the lip(More)
The I-vector approach to speaker recognition has become the prevalent paradigm over the past 2 years, showing top performance in NIST evaluations. This success is due mainly to the capability of the I-vector to capture and compress the speaker characteristics at low dimension and the subsequent channel compensation techniques that minimize channel(More)
This work aims to propose an efficient hardware/software system fo guassian mixture model (GMM) parts-based topology modeling for face identification and verification. Following its great success in speaker recognition, The GMM approach was extended to face recognition providing a good trade-off in terms of complexity, performance and robustness. Despite(More)
Nowadays, under controlled conditions the speaker verification systems based on the GMM-UBM paradigm show very good performance. However, in forensic investigation activities the conditions; in which recordings are acquired; are uncontrollable, a naive use of the baseline GMM-UBM system without feature normalization, model transformation and score(More)