Automatic face recognition in unconstrained environments is a challenging task. To test current trends in face recognition algorithms, we organized an evaluation on face recognition in mobile environment. This paper presents the results of 8 different participants using two verification metrics. Most submitted algorithms rely on one or more of three types of features: local binary patterns, Gabor wavelet responses including Gabor phases, and color information. The best results are obtained from UNILJ-ALP, which fused several image representations and feature types, and UC-HU, which learns optimal features with a convolutional neural network. Additionally, we assess the usability of the algorithms in mobile devices with limited resources.

El acceso a la versión del editor puede requerir la suscripción del recurso Access to the published version may require subscription AbstractThis paper evaluates the performance of the twelve primary systems submitted to the evaluation on speaker verification in the context of a mobile environment using the MOBIO database. The mobile environment provides a challenging and realistic test-bed for current state-of-the-art speaker verification techniques. Results in terms of equal error rate (EER), half total error rate (HTER) and detection error trade-off (DET) confirm that the best performing systems are based on total variability modeling, and are the fusion of several sub-systems. Nevertheless, the good old UBM-GMM based systems are still competitive. The results also show that the use of additional data for training as well as gender-dependent features can be helpful.

Similarity scores represent the basis for identity inference in biometric verification systems. However, because of the so-called miss-matched conditions across enrollment and probe samples and identity-dependent factors these scores typically exhibit statistical variations that affect the verification performance of biometric systems. To mitigate these variations, scorenormalisation techniques, such as the z-norm, the t-norm or the zt-norm, are commonly adopted. In this study, the authors study the problem of score normalisation in the scope of biometric verification and introduce a new class of non-parametric normalisation techniques, which make no assumptions regarding the shape of the distribution from which the scores are drawn (as the parametric techniques do). Instead, they estimate the shape of the score distribution and use the estimate to map the initial distribution to a common (predefined) distribution. Based on the new class of normalisation techniques they also develop a hybrid normalisation scheme that combines non-parametric and parametric techniques into hybrid two-step procedures. They evaluate the performance of the non-parametric and hybrid techniques in face-verification experiments on the FRGCv2 and SCFace databases and show that the non-parametric techniques outperform their parametric counterparts and that the hybrid procedure is not only feasible, but also retains some desirable characteristics from both the non-parametric and the parametric techniques. www.ietdl.org 62This is an open access article published by the IET under the Creative Commons Attribution License

This paper proposes a novel approach to light plane labeling in depthimage sensors relying on "uncoded" structured light. The proposed approach adopts probabilistic graphical models (PGMs) to solve the correspondence problem between the projected and the detected light patterns. The procedure for solving the correspondence problem is designed to take the spatial relations between the parts of the projected pattern and prior knowledge about the structure of the pattern into account, but it also exploits temporal information to achieve reliable light-plane labeling. The procedure is assessed on a database of light patterns detected with a specially developed imaging sensor that, unlike most existing solutions on the market, was shown to work reliably in outdoor environments as well as in the presence of other identical (active) sensors directed at the same scene. The results of our experiments show that the proposed approach is able to reliably solve the correspondence problem and assign light-plane labels to the detected pattern with a high accuracy, even when large spatial discontinuities are present in the observed scene.

Cloud computing represents one of the fastest growing areas of technology and offers a new computing model for various applications and services. This model is particularly interesting for the area of biometric recognition, where scalability, processing power, and storage requirements are becoming a bigger and bigger issue with each new generation of recognition technology. Next to the availability of computing resources, another important aspect of cloud computing with respect to biometrics is accessibility. Since biometric cloud services are easily accessible, it is possible to combine different existing implementations and design new multibiometric services that next to almost unlimited resources also offer superior recognition performance and, consequently, ensure improved security to its client applications. Unfortunately, the literature on the best strategies of how to combine existing implementations of cloud-based biometric experts into a multibiometric service is virtually nonexistent. In this paper, we try to close this gap and evaluate different strategies for combining existing biometric experts into a multibiometric cloud service. We analyze the (fusion) strategies from different perspectives such as performance gains, training complexity, or resource consumption and present results and findings important to software developers and other researchers working in the areas of biometrics and cloud computing. The analysis is conducted based on two biometric cloud services, which are also presented in the paper.