Sébastien Marcel

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Iris Liveness Detection Competition (LivDet-Iris) – The 2020 Edition

Sébastien Marcel, Amir Mohammadi, Priyanka Das

Launched in 2013, LivDet-Iris is an international competition series open to academia and industry with the aim to assess and report advances in iris Presentation Attack Detection (PAD). This paper presents results from the fourth competition of the series ...

IEEE2020

Generating Master Faces for Use in PerformingWolf Attacks on Face Recognition Systems

Sébastien Marcel

Due to its convenience, biometric authentication, especial face authentication, has become increasingly mainstream and thus is now a prime target for attackers. Presentation attacks and face morphing are typical types of attack. Previous research has shown ...

IEEE2020

Learning One Class Representations for Face Presentation Attack Detection using Multi-channel Convolutional Neural Networks

Sébastien Marcel

Face recognition has evolved as a widely used biometric modality. However, its vulnerability against presentation attacks poses a significant security threat. Though presentation attack detection (PAD) methods try to address this issue, they often fail in ...

IEEE2020

Vulnerability Analysis of Face Morphing Attacks from Landmarks and Generative Adversarial Networks

Sébastien Marcel, Eklavya Sarkar, Laurent Colbois

Morphing attacks is a threat to biometric systems where the biometric reference in an identity document can be altered. This form of attack presents an important issue in applications relying on identity documents such as border security or access control. ...

Idiap2020

CNN Patch Pooling for Detecting 3D Mask Presentation Attacks in NIR

Sébastien Marcel

Presentation attacks using 3D masks pose a serious threat to face recognition systems. Automatic detection of these attacks is challenging due to hyper-realistic nature of masks. In this work, we consider presentations acquired in near infrared (NIR) imagi ...

IEEE2020

Deep Models and Shortwave Infrared Information to Detect Face Presentation Attacks

Sébastien Marcel, Zohreh Mostaani, Guillaume Heusch

This paper addresses the problem of face presentation attack detection using different image modalities. In particular, the usage of short wave infrared (SWIR) imaging is considered. Face presentation attack detection is performed using recent models based ...

IEEE2020

Biometric Face Presentation Attack Detection with Multi-Channel Convolutional Neural Network

Sébastien Marcel, André Anjos, Zohreh Mostaani

Face recognition is a mainstream biometric authentication method. However, vulnerability to presentation attacks (a.k.a spoofing) limits its usability in unsupervised applications. Even though there are many methods available for tackling presentation atta ...

2020

Heterogeneous Face Recognition Using Domain Specific Units

Sébastien Marcel, Tiago De Freitas Pereira, André Anjos

The task of Heterogeneous Face Recognition consists in matching face images that are sensed in different domains, such as sketches to photographs (visual spectra images), thermal images to photographs or near-infrared images to photographs. In this work we ...

2019

Multispectral Deep Embeddings As a Countermeasure To Custom Silicone Mask Presentation Attacks

Sébastien Marcel

This work focuses on detecting presentation attacks (PA) mounted using custom silicone masks. Face recognition (FR) systems have been shown to be highly vulnerable to PAs based on such masks [1, 2]. Here we explore the use of multispectral data (color imag ...

2019

A Comprehensive Experimental and Reproducible Study on Selfie Biometrics in Multistream and Heterogeneous Settings

Sébastien Marcel, Guillaume Heusch, Tiago De Freitas Pereira

This contribution presents a new database to address current challenges in face recognition. It contains face video sequences of 75 individuals acquired either through a laptop webcam or when mimicking the front-facing camera of a smartphone. Sequences hav ...

2019

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Learning One Class Representations for Face Presentation Attack Detection using Multi-channel Convolutional Neural Networks

Sébastien Marcel

IEEE2020