Mobile Biometry (MOBIO) Face and Speaker Verification Evaluation

This paper evaluates the performance of face and speaker verification techniques in the context of a mobile environment. The mobile environment was chosen as it provides a realistic and challenging test-bed for biometric person verification techniques to operate. For instance the audio environment is quite noisy and there is limited control over the illumination conditions and the pose of the subject for the video. To conduct this evaluation, a part of a database captured during the ``Mobile Biometry'' (MOBIO) European Project was used. In total there were nine participants to the evaluation who submitted a face verification system and five participants who submitted speaker verification systems. The nine face verification systems all varied significantly in terms of both verification algorithms and face detection algorithms. Several systems used the OpenCV face detector while the better systems used proprietary software for the task of face detection. This ended up making the evaluation of verification algorithms challenging. The five speaker verification systems were based on one of two paradigms: a Gaussian Mixture Model (GMM) or Support Vector Machine (SVM) paradigm. In general the systems based on the SVM paradigm performed better than those based on the GMM paradigm.

A Cross-database Study of Voice Presentation Attack Detection

Sébastien Marcel

Despite an increasing interest in speaker recognition technologies, a significant obstacle still hinders their wide deployment --- their high vulnerability to spoofing or presentation attacks. These attacks can be easy to perform. For instance, if an attacker has access to a speech sample from a target user, he/she can replay it using a loudspeaker or a smartphone to the recognition system during the authentication process. The ease of executing presentation attacks and the fact that no technical knowledge of the biometric system is required makes these attacks especially threatening in practical application. Therefore, late research focuses on collecting data databases with such attacks and on development of presentation attack detection (PAD) systems. In this chapter, we present an overview of the latest databases and the techniques to detect presentation attacks. We consider several prominent databases that contain bona fide and attack data, including: ASVspoof 2015, ASVspoof 2017, AVspoof, voicePA, and BioCPqD-PA (the only proprietary database). Using these databases, we focus on the performance of PAD systems in the cross-database scenario or in the presence of 'unknown' (not available during training) attacks, as these scenarios are closer to practice, when pre-trained systems need to detect attacks in unforeseen conditions. We first present and discuss the performance of PAD systems based on handcrafted features and traditional Gaussian mixture model (GMM) classifiers. We then demonstrate whether the score fusion techniques can improve the performance of PADs. We also present some of the latest results of using neural networks for presentation attack detection. The experiments show that PAD systems struggle to generalize across databases and mostly unable to detect unknown attacks, with systems based on neural networks demonstrating better performance compared to the systems based on handcraft features.

Springer2018

Scalable Probabilistic Models for Face and Speaker Recognition

Laurent El Shafey

In the biometrics community, face and speaker recognition are mature fields in which several systems have been proposed over the past twenty years. While existing systems perform well under controlled recording conditions, mismatch caused by the use of different sensors or a lack of cooperation from the subject still significantly affects performance, especially in challenging scenarios such as in forensics. Furthermore, existing methods suffer from scalability issues, which prevents them from taking advantage of increasingly large amounts of training data. This is otherwise a promising approach to improve accuracy in such challenging scenarios. In this thesis we address these problems of mismatch and complexity by developing scalable probabilistic models that we apply to face, speaker and bimodal recognition. Our contributions are four-fold. First, we propose a unified framework for session variability modeling techniques based on Gaussian mixture models (GMM), that encompasses inter-session variability (ISV) modeling, joint factor analysis (JFA) and total variability (TV) modeling. Second, we propose a novel exact and scalable formulation of probabilistic linear discriminant analysis (PLDA), which is a probabilistic and generative framework that models between-class and within-class variations. This formulation solves a major scalability issue, by improving both the time complexity of the training procedure from cubic to linear with respect to the number of samples per class, and the complexity of the scoring procedure. Furthermore, the implementations of all the proposed techniques are integrated into a novel collaborative open source software library called Bob 1 that enforces fair evaluations and encourages reproducible research. Fourth and finally, large-scale experiments are conducted with all of the above machine learning algorithms on several databases such as FRGC for face recognition, NIST SRE12 for speaker recognition and MOBIO for bimodal recognition, showing competitive performances.

EPFL2014

On Performance / Robustness / Complexity Trade-Offs in Face Verification

Samy Bengio

In much of the literature devoted to face recognition, experiments are performed with controlled images (e.g. manual face localization, controlled lighting, background and pose); however, a practical recognition system has to be robust to more challenging conditions. In this paper we first evaluate, on the relatively difficult BANCA database, the performance, robustness and complexity of Gaussian Mixture Model (GMM), 1D- and pseudo-2D Hidden Markov Model (HMM) based systems, using both manual and automatic face localization. We also propose to extend the GMM approach through the use of local features with embedded positional information, increasing performance without sacrificing its low complexity. Experiments show that good performance on manually located faces is not necessarily indicative of good performance on automatically located faces (which are imperfectly located). The deciding factor is shown to be the degree of constraints placed on spatial relations between face parts. Methods which utilize rigid constraints have poor robustness compared to methods which have relaxed constraints. Furthermore, we show that while the pseudo-2D HMM approach has the best overall performance, classification time on current hardware makes it impractical. The best trade-off in terms of complexity, robustness and discrimination performance is achieved by the extended GMM approach.