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Auflistung nach Autor:in "Marcel, Sébastien"

1 - 6 von 6
  • Konferenzbeitrag
    Face verification using Gabor filtering and adapted Gaussian mixture models
    (BIOSIG 2012, 2012) Shafey, Laurent El; Wallace, Roy; Marcel, Sébastien
    The search for robust features for face recognition in uncontrolled environments is an important topic of research. In particular, there is a high interest in Gaborbased features which have invariance properties to simple geometrical transformations. In this paper, we first reinterpret Gabor filtering as a frequency decomposition into bands, and analyze the influence of each band separately for face recognition. Then, a new face verification scheme is proposed, combining the strengths of Gabor filtering with Gaussian Mixture Model (GMM) modelling. Finally, this new system is evaluated on the BANCA and MOBIO databases with respect to well known face recognition algorithms. The proposed system demonstrates up to 52\% relative improvement in verification error rate compared to a standard GMM approach, and outperforms the state-of-the-art Local Gabor Binary Pattern Histogram Sequence (LGBPHS) technique for several face verification protocols on two different databases.
  • Konferenzbeitrag
    On the effectiveness of local binary patterns in face anti-spoofing
    (BIOSIG 2012, 2012) Chingovska, Ivana; Anjos, André; Marcel, Sébastien
    Spoofing attacks are one of the security traits that biometric recognition systems are proven to be vulnerable to. When spoofed, a biometric recognition system is bypassed by presenting a copy of the biometric evidence of a valid user. Among all biometric modalities, spoofing a face recognition system is particularly easy to perform: all that is needed is a simple photograph of the user. In this paper, we address the problem of detecting face spoofing attacks. In particular, we inspect the potential of texture features based on Local Binary Patterns (LBP) and their variations on three types of attacks: printed photographs, and photos and videos displayed on electronic screens of different sizes. For this purpose, we introduce REPLAY-ATTACK, a novel publicly available face spoofing database which contains all the mentioned types of attacks. We conclude that LBP, with ~15% Half Total Error Rate, show moderate discriminability when confronted with a wide set of attack types.
  • Konferenzbeitrag
    On the vulnerability of finger vein recognition to spoofing
    (BIOSIG 2014, 2014) Tome, Pedro; Vanoni, Matthias; Marcel, Sébastien
    The vulnerability of finger vein recognition to spoofing is studied in this paper. A collection of spoofing finger vein images has been created from real finger vein samples. Finger vein images are printed using a commercial printer and then, presented at an open source finger vein sensor. Experiments are carried out using an extensible framework, which allows fair and reproducible benchmarks. Experimental results lead to a spoofing false accept rate of 86\%, thus showing that finger vein biometrics is vulnerable to spoofing attacks, pointing out the importance to investigate countermeasures against this type of fraudulent actions.
  • Konferenzbeitrag
    Palm vein database and experimental framework for reproducible research
    (BIOSIG 2015, 2015) Tome, Pedro; Marcel, Sébastien
    A palm vein database acquired by a contactless sensor together with an experimental framework freely available for fair reproducible research purposes are described. The palm vein recognition system uses automatic palm region segmentation and circular Gabor filter approach to enhance the veins in the preprocessing, LBP features and histogram intersection as matching. Results are presented comparing two automatic segmentation using the ROI-1 region proportioned by the acquisition sensor and the ROI-2 region generated by the recognition software developed. Complete benchmark results using popular methods and the source code are attached to the database as a reference for other researchers.
  • Konferenzbeitrag
    Spoofing 2D face recognition systems with 3D masks
    (BIOSIG 2013, 2013) Erdogmus, Nesli; Marcel, Sébastien
    Vulnerability to spoofing attacks is a serious drawback for many biometric systems. Among all biometric traits, face is the one that is exposed to the most serious threat, since it is exceptionally easy to access. The limited work on fraud detection capabilities for face mainly shapes around 2D attacks forged by displaying printed photos or replaying recorded videos on mobile devices. A significant portion of this work is based on the flatness of the facial surface in front of the sensor. In this study, we complicate the spoofing problem further by introducing the 3rd dimension and examine possible 3D attack instruments. A small database is constructed with six different types of 3D facial masks and experimented on to determine the right direction to study 3D attacks. Spoofing performance for each type of mask is assessed and analysed thoroughly using two Gabor-wavelet-based algorithms.
  • Konferenzbeitrag
    Vein Enhancement with Deep Auto-Encoders to improve Finger Vein Recognition
    (BIOSIG 2021 - Proceedings of the 20th International Conference of the Biometrics Special Interest Group, 2021) Bros, Victor; Kotwal, Ketan; Marcel, Sébastien
    The field of Vascular Biometric Recognition has drawn a lot of attention recently with the emergence of new computer vision techniques. The different methods using Deep Learning involve a new understanding of deeper features from the vascular network. The specific architecture of the veins needs complex model capable of comprehending the vascular pattern. In this paper, we present an image enhancement method using Deep Convolutional Neural Network. For this task, a residual convolutional auto-encoder architecture has been trained in a supervised way to enhance the vein patterns in near-infrared images. The method has been evaluated on several databases with promising results on the UTFVP database as a main result. In including the model as a preprocessing in the biometric pipelines of recognition for finger vein patterns, the error rate has been reduced from 2.1% to 1.0%.