We study the problem of score normalization in biometric verification systems. Specifically, we introduce a new class of normalization techniques, which unlike the commonly used parametric score normalization techniques, such as z- or t-norm, make no assumptions regarding the shape of the underlying score distribution. The proposed class of normalization techniques first estimates the relevant score distribution in an impostor-centric manner using kernel density estimation and then maps the estimated distribution to a common one. Our experimental results obtained on the FRGCv2 face database show that the proposed non-parametric score normalization techniques consistently outperform their parametric counterparts when the target distribution takes a log-normal form and that all assessed techniques, i.e., z-, t-, zt- and tz-norms, improve upon the setting where no score normalization is used. We demonstrate that the normalization contribute significantly to the robustness of the recognition system, when missmatched conditions caused, for example, by lighting or ageing effects, between probe and gallery images are present.
COBISS.SI-ID: 9520468
Face recognition in uncontrolled environments remains an open problem that has not been satisfactorily solved by existing recognition techniques. In this paper, we tackle this problem using a variant of the recently proposed Probabilistic Linear Discriminant Analysis (PLDA). We show that simplified versions of the PLDA model, which are regularly used in the field of speaker recognition, rely on certain assumptions that not only result in a simpler PLDA model, but also reduce the computational load of the technique and ‐ as indicated by our experimental assessments ‐ improve recognition performance. Moreover, we show that, contrary to the general belief that PLDA‐based methods produce well calibrated verification scores, score normalization techniques can still deliver significant performance gains, but only if nonparametric score normalization techniques are employed. Last but not least, we demonstrate the competitiveness of the simplified PLDA model for face recognition by comparing our results with the state‐of‐the‐art results from the literature obtained on the second version of the large‐scale Face Recognition Grand Challenge (FRGC) database.
COBISS.SI-ID: 9520724
The paper introduces a novel framework for 3D face recognition that capitalizes on region covariance descriptors and Gaussian mixture models. The framework presents an elegant and coherent way of combining multiple facial representations, while simultaneously examining all computed representations at various levels of locality. The framework first computes a number of region covariance matrices/descriptors from different sized regions of several image representations and then adopts the unscented transform to derive low-dimensional feature vectors from the computed descriptors. By doing so, it enables computations in the Euclidean space, and makes Gaussian mixture modeling feasible. In the last step a support vector machine classification scheme is used to make a decision regarding the identity of the modeled input 3D face image. The proposed framework exhibits several desirable characteristics, such as an inherent mechanism for data fusion/integration (through the region covariance matrices), the ability to examine the facial images at different levels of locality, and the ability to integrate domain-specific prior knowledge into the modeling procedure. We assess the feasibility of the proposed framework on the Face Recognition Grand Challenge version 2 (FRGCv2) database with highly encouraging results. Note that the work presented in this paper could easily be applied to other biometric (image) modalities, such as 2d face images, as well, as depth images can be considered as a form of intensity images.
COBISS.SI-ID: 9519444